2020.07.28.txt

==========New Papers==========
1, TITLE:       COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature
http://arxiv.org/abs/2007.12731
AUTHORS:        Colby Wise ; Vassilis N. Ioannidis ; Miguel Romero Calvo ; Xiang Song ; George Price ; Ninad Kulkarni ; Ryan Brand ; Parminder Bhatia ; George Karypis
HIGHLIGHT:      In this work, we present the COVID-19 Knowledge Graph (CKG), a heterogeneous graph for extracting and visualizing complex relationships between COVID-19 scientific articles.

2, TITLE:       Feature visualization of Raman spectrum analysis with deep convolutional neural network
http://arxiv.org/abs/2007.13354
AUTHORS:        Masashi Fukuhara ; Kazuhiko Fujiwara ; Yoshihiro Maruyama ; Hiroyasu Itoh
HIGHLIGHT:      We demonstrate a recognition and feature visualization method that uses a deep convolutional neural network for Raman spectrum analysis.

3, TITLE:       Build Scripts with Perfect Dependencies
http://arxiv.org/abs/2007.12737
AUTHORS:        Sarah Spall ; Neil Mitchell ; Sam Tobin-Hochstadt
HIGHLIGHT:      We outline a build system where dependencies are not specified, but instead captured by tracing execution.

4, TITLE:       IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines
http://arxiv.org/abs/2007.12733
AUTHORS:        Soroush Javdan ; Taha Shangipour ataei ; Behrouz Minaei-Bidgoli
HIGHLIGHT:      We used different preprocessing techniques and proposed to use different methods that vary from NBSVM to more complicated deep neural network models.

5, TITLE:       Online spatio-temporal learning in deep neural networks
http://arxiv.org/abs/2007.12723
AUTHORS:        Thomas Bohnstingl ; Stanisław Woźniak ; Wolfgang Maass ; Angeliki Pantazi ; Evangelos Eleftheriou
COMMENTS:       Main manuscript: 8 pages, 3 figures, 1 table, Supplementary notes: 11 pages
HIGHLIGHT:      Here we present an alternative perspective that is based on a clear separation of spatial and temporal gradient components.

6, TITLE:       Constructing a Testbed for Psychometric Natural Language Processing
http://arxiv.org/abs/2007.12969
AUTHORS:        Ahmed Abbasi ; David G. Dobolyi ; Richard G. Netemeyer
COMMENTS:       7 pages, 9 figures
HIGHLIGHT:      In this paper, we describe our efforts to construct a corpus for psychometric natural language processing (NLP).

7, TITLE:       Towards Purely Unsupervised Disentanglement of Appearance and Shape for Person Images Generation
http://arxiv.org/abs/2007.13098
AUTHORS:        Hongtao Yang ; Tong Zhang ; Wenbing Huang ; Xuming He
HIGHLIGHT:      In this paper, we aim to address this challenge in a more unsupervised manner---we do not require any annotation nor any external task-specific clues.

8, TITLE:       U2-ONet: A Two-level Nested Octave U-structure with Multiscale Attention Mechanism for Moving Instances Segmentation
http://arxiv.org/abs/2007.13092
AUTHORS:        Chenjie Wang ; Chengyuan Li ; Bin Luo
COMMENTS:       10 pages, 7 figures,
HIGHLIGHT:      In order to efficiently segment out all moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested Octave U-structure network with a multiscale attention mechanism called U2-ONet.

9, TITLE:       MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement
http://arxiv.org/abs/2007.12944
AUTHORS:        Rinon Gal ; Amit Bermano ; Hao Zhang ; Daniel Cohen-Or
HIGHLIGHT:      We present MRGAN, a multi-rooted adversarial network which generates part-disentangled 3D point-cloud shapes without part-based shape supervision.

10, TITLE:      Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression
http://arxiv.org/abs/2007.12946
AUTHORS:        Ted Pedersen
COMMENTS:       10 pages, To appear in the Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval--2020), December 12-13, 2020, Barcelona (a COLING-2020 workshop, aka OffensEval--2020)
HIGHLIGHT:      This paper describes the Duluth systems that participated in SemEval--2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval--2020).

11, TITLE:      Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets
http://arxiv.org/abs/2007.12949
AUTHORS:        Ted Pedersen
COMMENTS:       7 pages, Appears in the Proceedings of the 13th International Workshop on Semantic Eva luation (SemEval 2019), June 2019, pp. 593-599, Minneapolis, MN (a NAACL-2019 workshop, aka OffenseEval--2019)
HIGHLIGHT:      This paper describes the Duluth systems that participated in SemEval--2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval).

12, TITLE:      MACU-Net Semantic Segmentation from High-Resolution Remote Sensing Images
http://arxiv.org/abs/2007.13083
AUTHORS:        Rui Li ; Chenxi Duan ; Shunyi Zheng
HIGHLIGHT:      In this paper, based on U-Net and asymmetric convolution block, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected architecture, MACU-Net, for semantic segmentation using high-resolution remote sensing images.

13, TITLE:      Multitape automata and finite state transducers with lexicographic weights
http://arxiv.org/abs/2007.12940
AUTHORS:        Aleksander Mendoza-Drosik
HIGHLIGHT:      Multitape automata and finite state transducers with lexicographic weights

14, TITLE:      Gradient Regularized Contrastive Learning for Continual Domain Adaptation
http://arxiv.org/abs/2007.12942
AUTHORS:        Peng Su ; Shixiang Tang ; Peng Gao ; Di Qiu ; Ni Zhao ; Xiaogang Wang
HIGHLIGHT:      In this work, we propose Gradient Regularized Contrastive Learning to solve the above obstacles.

15, TITLE:      A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges
http://arxiv.org/abs/2007.13069
AUTHORS:        Bin Fu ; Yunqi Qiu ; Chengguang Tang ; Yang Li ; Haiyang Yu ; Jian Sun
COMMENTS:       19 pages, 5 figures
HIGHLIGHT:      After describing the methods of these branches, we analyze directions for future research and introduce the models proposed by the Alime team.

16, TITLE:      Approaches of large-scale images recognition with more than 50,000 categoris
http://arxiv.org/abs/2007.13072
AUTHORS:        Wanhong Huang ; Rui Geng
HIGHLIGHT:      In this paper, we provide a viable solution for classifying large-scale species datasets using traditional CV techniques such as.features extraction and processing, BOVW(Bag of Visual Words) and some statistical learning technics like Mini-Batch K-Means,SVM which are used in our works.

17, TITLE:      Bounded Fuzzy Possibilistic Method of Critical Objects Processing in Machine Learning
http://arxiv.org/abs/2007.13077
AUTHORS:        Hossein Yazdani
HIGHLIGHT:      The Thesis introduces a new type of object, called critical, as well as categorizing data objects into two different categories: structural-based and behavioural-based. Then the Thesis proposes new sets of similarity functions, called Weighted Feature Distance (WFD) and Prioritized Weighted Feature Distance (PWFD).

18, TITLE:      SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction
http://arxiv.org/abs/2007.13078
AUTHORS:        Sriram N N ; Buyu Liu ; Francesco Pittaluga ; Manmohan Chandraker
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      We propose advances that address two key challenges in future trajectory prediction: (i) multimodality in both training data and predictions and (ii) constant time inference regardless of number of agents.

19, TITLE:      Robust Collective Classification against Structural Attacks
http://arxiv.org/abs/2007.13073
AUTHORS:        Kai Zhou ; Yevgeniy Vorobeychik
COMMENTS:       UAI 2020
HIGHLIGHT:      Collective learning methods exploit relations among data points to enhance classification performance.

20, TITLE:      Insightful Assistant: AI-compatible Operation Graph Representations for Enhancing Industrial Conversational Agents
http://arxiv.org/abs/2007.12929
AUTHORS:        Bekir Bayrak ; Florian Giger ; Christian Meurisch
HIGHLIGHT:      In the light of these deficits, this paper presents Insightful Assistant---a pipeline concept based on a novel operation graph representation resulting from the intents detected. We further collected a unique crowd-sourced set of 869 requests, each with four different variants expected visualization, for an industrial dataset.

21, TITLE:      Video Super Resolution Based on Deep Learning: A comprehensive survey
http://arxiv.org/abs/2007.12928
AUTHORS:        Hongying Liu ; Zhubo Ruan ; Peng Zhao ; Fanhua Shang ; Linlin Yang ; Yuanyuan Liu
HIGHLIGHT:      In this survey, we comprehensively investigate 28 state-of-the-art video super-resolution methods based on deep learning.

22, TITLE:      WGANVO: Monocular Visual Odometry based on Generative Adversarial Networks
http://arxiv.org/abs/2007.13704
AUTHORS:        Javier Cremona ; Lucas Uzal ; Taihú Pire
HIGHLIGHT:      In this work we present WGANVO, a Deep Learning based monocular Visual Odometry method.

23, TITLE:      Tighter risk certificates for neural networks
http://arxiv.org/abs/2007.12911
AUTHORS:        María Pérez-Ortiz ; Omar Rivasplata ; John Shawe-Taylor ; Csaba Szepesvári
COMMENTS:       Preprint under review
HIGHLIGHT:      This paper presents empirical studies regarding training probabilistic neural networks using training objectives derived from PAC-Bayes bounds.

24, TITLE:      Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios
http://arxiv.org/abs/2007.13705
AUTHORS:        Anca Avram ; Oliviu Matei ; Camelia Pintea ; Carmen Anton
COMMENTS:       15 figures
HIGHLIGHT:      the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM).

25, TITLE:      Modal Uncertainty Estimation via Discrete Latent Representation
http://arxiv.org/abs/2007.12858
AUTHORS:        Di Qiu ; Lok Ming Lui
HIGHLIGHT:      In this work we introduce such a deep learning framework that learns the one-to-many mappings between the inputs and outputs, together with faithful uncertainty measures.

26, TITLE:      NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification
http://arxiv.org/abs/2007.12913
AUTHORS:        Ilya Dimov ; Vladislav Korzun ; Ivan Smurov
HIGHLIGHT:      This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles.

27, TITLE:      Bollyrics: Automatic Lyrics Generator for Romanised Hindi
http://arxiv.org/abs/2007.12916
AUTHORS:        Naman Jain ; Ankush Chauhan ; Atharva Chewale ; Ojas Mithbavkar ; Ujjaval Shah ; Mayank Singh
COMMENTS:       Submitted to NLP4MusA Workshop, ISMIR'2020
HIGHLIGHT:      We propose simple techniques to capture rhyming patterns before and during the model training process in Hindi language.

28, TITLE:      Crowdsourced 3D Mapping: A Combined Multi-View Geometry and Self-Supervised Learning Approach
http://arxiv.org/abs/2007.12918
AUTHORS:        Hemang Chawla ; Matti Jukola ; Terence Brouns ; Elahe Arani ; Bahram Zonooz
COMMENTS:       Accepted at 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
HIGHLIGHT:      In this work, we propose a framework that estimates the 3D positions of semantically meaningful landmarks such as traffic signs without assuming known camera intrinsics, using only monocular color camera and GPS.

29, TITLE:      MMDF: Mobile Microscopy Deep Framework
http://arxiv.org/abs/2007.13701
AUTHORS:        Anatasiia Kornilova ; Mikhail Salnikov ; Olga Novitskaya ; Maria Begicheva ; Egor Sevriugov ; Kirill Shcherbakov
HIGHLIGHT:      In current study we applied image processing techniques from Deep Learning (in-focus/out-of-focus classification, image deblurring and denoising, multi-focus image fusion) to the data obtained from the mobile microscope.

30, TITLE:      A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds
http://arxiv.org/abs/2007.12831
AUTHORS:        Yi Wang ; Junhui Hou ; Xinyu Hou ; Lap-Pui Chau
COMMENTS:       10 pages
HIGHLIGHT:      In this paper, we propose a novel self-training approach which enables a typical object detector trained only with point-level annotations (i.e., objects are labeled with points) to estimate both the center points and sizes of crowded objects.

31, TITLE:      Multi-Armed Bandits for Minesweeper: Profiting from Exploration-Exploitation Synergy
http://arxiv.org/abs/2007.12824
AUTHORS:        Igor Q. Lordeiro ; Douglas O. Cardoso
COMMENTS:       Submitted to IEEE Transactions on Games (ISSN 2475-1510 / 2475-1502)
HIGHLIGHT:      Despite this fact the main contribution of this work is a detailed examination of Minesweeper from a learning perspective, which led to various original insights which are thoroughly discussed.

32, TITLE:      Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering
http://arxiv.org/abs/2007.12829
AUTHORS:        Shi-Xun Lina ; Guo Zhongb ; Ting Shu
HIGHLIGHT:      To address the above issues, we propose a novel multi-view subspace clustering method via simultaneously assigning weights for different features and capturing local information of data in view-specific self-representation feature spaces.

33, TITLE:      All-Optical Information Processing Capacity of Diffractive Surfaces
http://arxiv.org/abs/2007.12813
AUTHORS:        Onur Kulce ; Deniz Mengu ; Yair Rivenson ; Aydogan Ozcan
COMMENTS:       28 Pages, 6 Figures, 1 Table
HIGHLIGHT:      Here, we analyze the information processing capacity of coherent optical networks formed by diffractive surfaces that are trained to perform an all-optical computational task between a given input and output field-of-view.

34, TITLE:      Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
http://arxiv.org/abs/2007.12803
AUTHORS:        Xiaofeng Gao ; Ran Gong ; Yizhou Zhao ; Shu Wang ; Tianmin Shu ; Song-Chun Zhu
COMMENTS:       IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2020), 8 pages, 9 figures
HIGHLIGHT:      Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state.

35, TITLE:      Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild
http://arxiv.org/abs/2007.12806
AUTHORS:        Minh Vo ; Yaser Sheikh ; Srinivasa G. Narasimhan
COMMENTS:       Accepted to IEEE TPAMI
HIGHLIGHT:      We present a spatiotemporal bundle adjustment framework that jointly optimizes four coupled sub-problems: estimating camera intrinsics and extrinsics, triangulating static 3D points, as well as sub-frame temporal alignment between cameras and computing 3D trajectories of dynamic points.

36, TITLE:      Counting Fish and Dolphins in Sonar Images Using Deep Learning
http://arxiv.org/abs/2007.12808
AUTHORS:        Stefan Schneider ; Alex Zhuang
COMMENTS:       19 pages, 5 figures, 1 table
HIGHLIGHT:      We propose a novel approach to calculating fish abundance using deep learning for fish and dolphin estimates from sonar images taken from the back of a trolling boat.

37, TITLE:      Efficient Generation of Structured Objects with Constrained Adversarial Networks
http://arxiv.org/abs/2007.13197
AUTHORS:        Luca Di Liello ; Pierfrancesco Ardino ; Jacopo Gobbi ; Paolo Morettin ; Stefano Teso ; Andrea Passerini
HIGHLIGHT:      As a remedy, we propose Constrained Adversarial Networks (CANs), an extension of GANs in which the constraints are embedded into the model during training.

38, TITLE:      Do recommender systems function in the health domain: a system review
http://arxiv.org/abs/2007.13058
AUTHORS:        Jia Su ; Yi Guan ; Yuge Li ; Weile Chen ; He Lv ; Yageng Yan
COMMENTS:       32 pages, 1 table, 1 figure, 38 discussed articles
HIGHLIGHT:      In this paper, we review aspects of health recommender systems including interests, methods, evaluation, future challenges and trend issues.

39, TITLE:      Reed at SemEval-2020 Task 9: Sentiment Analysis on Code-Mixed Tweets
http://arxiv.org/abs/2007.13061
AUTHORS:        Vinay Gopalan ; Mark Hopkins
HIGHLIGHT:      During the evaluation phase of the competition, we obtained an F-score of 71.3% with our best model, which placed $4^{th}$ out of 62 entries in the official system rankings.

40, TITLE:      A Dual Iterative Refinement Method for Non-rigid Shape Matching
http://arxiv.org/abs/2007.13049
AUTHORS:        Rui Xiang ; Rongjie Lai ; Hongkai Zhao
COMMENTS:       10 pages, 11 figures and 1 table
HIGHLIGHT:      In this work, a simple and efficient dual iterative refinement (DIR) method is proposed for dense correspondence between two nearly isometric shapes.

41, TITLE:      Cyclotomic Identity Testing and Applications
http://arxiv.org/abs/2007.13179
AUTHORS:        Nikhil Balaji ; Sylvain Perifel ; Mahsa Shirmohammadi ; James Worrell
HIGHLIGHT:      We consider the cyclotomic identity testing problem: given a polynomial $f(x_1,\ldots,x_k)$, decide whether $f(\zeta_n^{e_1},\ldots,\zeta_n^{e_k})$ is zero, for $\zeta_n = e^{2\pi i/n}$ a primitive complex $n$-th root of unity and integers $e_1,\ldots,e_k$.

42, TITLE:      Recursive Rules with Aggregation: A Simple Unified Semantics
http://arxiv.org/abs/2007.13053
AUTHORS:        Yanhong A. Liu ; Scott D. Stoller
HIGHLIGHT:      This paper describes a unified semantics for recursive rules with aggregation, extending the unified founded semantics and constraint semantics for recursive rules with negation.

43, TITLE:      Human Preference Scaling with Demonstrations For Deep Reinforcement Learning
http://arxiv.org/abs/2007.12904
AUTHORS:        Zehong Cao ; KaiChiu Wong ; Chin-Teng Lin
HIGHLIGHT:      Human Preference Scaling with Demonstrations For Deep Reinforcement Learning

44, TITLE:      CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis
http://arxiv.org/abs/2007.12909
AUTHORS:        Mauro Barni ; Kassem Kallas ; Ehsan Nowroozi ; Benedetta Tondi
COMMENTS:       6 pages, 2 figures, 4 tables, WIFS 2020 NY USA
HIGHLIGHT:      In this paper, we propose a method for distinguishing GAN-generated from natural images by exploiting inconsistencies among spectral bands, with specific focus on the generation of synthetic face images.

45, TITLE:      Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding
http://arxiv.org/abs/2007.13027
AUTHORS:        Manar D. Samad ; Nalin D. Khounviengxay ; Megan A. Witherow
COMMENTS:       14 pages, 3 figures, 7 tables
HIGHLIGHT:      This paper investigates the effect of seven text processing scenarios on a particular text domain (Twitter) and application (sentiment classification).

46, TITLE:      Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement
http://arxiv.org/abs/2007.13024
AUTHORS:        Jun Qi ; Hu Hu ; Yannan Wang ; Chao-Han Huck Yang ; Sabato Marco Siniscalchi ; Chin-Hui Lee
COMMENTS:       Accepted to InterSpeech 2020
HIGHLIGHT:      This paper investigates different trade-offs between the number of model parameters and enhanced speech qualities by employing several deep tensor-to-vector regression models for speech enhancement.

47, TITLE:      A Preliminary Exploration into an Alternative CellLineNet: An Evolutionary Approach
http://arxiv.org/abs/2007.13044
AUTHORS:        Akwarandu Ugo Nwachuku ; Xavier Lewis-Palmer ; Darlington Ahiale Akogo
HIGHLIGHT:      This paper presents an on-going exploratory approach to neural network architecture design and is presented for further study.

48, TITLE:      Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve
http://arxiv.org/abs/2007.13034
AUTHORS:        Weicheng Kuo ; Anelia Angelova ; Tsung-Yi Lin ; Angela Dai
COMMENTS:       ECCV 2020 (Spotlight)
HIGHLIGHT:      We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an image by constructing a CAD-based representation of the objects and their poses.

49, TITLE:      Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics
http://arxiv.org/abs/2007.13005
AUTHORS:        Daniel Kang ; Ankit Mathur ; Teja Veeramacheneni ; Peter Bailis ; Matei Zaharia
HIGHLIGHT:      In this work, we examine end-to-end DNN execution in visual analytics systems on modern accelerators.

50, TITLE:      Unsupervised Subword Modeling Using Autoregressive Pretraining and Cross-Lingual Phone-Aware Modeling
http://arxiv.org/abs/2007.13002
AUTHORS:        Siyuan Feng ; Odette Scharenborg
COMMENTS:       5 pages, 3 figures. Accepted for publication in INTERSPEECH 2020, Shanghai, China
HIGHLIGHT:      A second aim of this work is to investigate the robustness of our approach's effectiveness to different amounts of training data.

51, TITLE:      Robust and Generalizable Visual Representation Learning via Random Convolutions
http://arxiv.org/abs/2007.13003
AUTHORS:        Zhenlin Xu ; Deyi Liu ; Junlin Yang ; Marc Niethammer
HIGHLIGHT:      Hence, our goal is to train models in such a way that improves their robustness to these perturbations.

52, TITLE:      Style is a Distribution of Features
http://arxiv.org/abs/2007.13010
AUTHORS:        Eddie Huang ; Sahil Gupta
HIGHLIGHT:      We present a new algorithm for style transfer that fully extracts the style from the features by redefining the style loss as the Wasserstein distance between the distribution of features.

53, TITLE:      HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
http://arxiv.org/abs/2007.13007
AUTHORS:        Sachin Mehta ; Ximing Lu ; Donald Weaver ; Joann G. Elmore ; Hannaneh Hajishirzi ; Linda Shapiro
COMMENTS:       10 pages
HIGHLIGHT:      In this paper, we introduce a novel attention-based network, the Holistic ATtention Network (HATNet) to classify breast biopsy images.

54, TITLE:      Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry
http://arxiv.org/abs/2007.13393
AUTHORS:        Yifan Xu ; Tianqi Fan ; Yi Yuan ; Gurprit Singh
COMMENTS:       European Conference on Computer Vision 2020 (ECCV 2020)
HIGHLIGHT:      Based on Farthest Point Sampling algorithm, we propose a sampling scheme that theoretically encourages better generalization performance, and results in fast convergence for SGD-based optimization algorithms.

55, TITLE:      NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination
http://arxiv.org/abs/2007.13376
AUTHORS:        Penghao Zhou ; Chong Zhou ; Pai Peng ; Junlong Du ; Xing Sun ; Xiaowei Guo ; Feiyue Huang
COMMENTS:       Accepted at the ACM International Conference on Multimedia (ACM MM) 2020
HIGHLIGHT:      Thus, we propose Nearby Objects Hallucinator (NOH), which pinpoints the objects nearby each proposal with a Gaussian distribution, together with NOH-NMS, which dynamically eases the suppression for the space that might contain other objects with a high likelihood.

56, TITLE:      ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks
http://arxiv.org/abs/2007.13384
AUTHORS:        Alexander Frickenstein ; Manoj-Rohit Vemparala ; Nael Fasfous ; Laura Hauenschild ; Naveen-Shankar Nagaraja ; Christian Unger ; Walter Stechele
COMMENTS:       Accepted by DAC'20
HIGHLIGHT:      In this paper, we propose the autoencoder-based low-rank filter-sharing technique technique (ALF).

57, TITLE:      Learning Compositional Neural Programs for Continuous Control
http://arxiv.org/abs/2007.13363
AUTHORS:        Thomas Pierrot ; Nicolas Perrin ; Feryal Behbahani ; Alexandre Laterre ; Olivier Sigaud ; Karim Beguir ; Nando de Freitas
HIGHLIGHT:      We propose a novel solution to challenging sparse-reward, continuous control problems that require hierarchical planning at multiple levels of abstraction.

58, TITLE:      Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds
http://arxiv.org/abs/2007.13344
AUTHORS:        Jinxian Liu ; Minghui Yu ; Bingbing Ni ; Ye Chen
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      We develop a novel learning scheme named Self-Prediction for 3D instance and semantic segmentation of point clouds.

59, TITLE:      Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation
http://arxiv.org/abs/2007.13296
AUTHORS:        Andrew Stephan ; Brian Gardner ; Steven J. Koester ; Andre Gruning
HIGHLIGHT:      In this work we propose a new supervised learning method for temporally-encoded multilayer spiking networks to perform classification.

60, TITLE:      Representation Learning with Video Deep InfoMax
http://arxiv.org/abs/2007.13278
AUTHORS:         Hjelm ; R Devon ;  Bachman ;  Philip
HIGHLIGHT:      In this paper, we extend DIM to the video domain by leveraging similar structure in spatio-temporal networks, producing a method we call Video Deep InfoMax(VDIM).

61, TITLE:      Algorithm Configurations of MOEA/D with an Unbounded External Archive
http://arxiv.org/abs/2007.13352
AUTHORS:        Lie Meng Pang ; Hisao Ishibuchi ; Ke Shang
HIGHLIGHT:      In this paper, we examine the design of MOEA/D under these two frameworks.

62, TITLE:      Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
http://arxiv.org/abs/2007.13332
AUTHORS:        Nan Zhuang ; Cheng Yang
COMMENTS:       Technical Report
HIGHLIGHT:      In this paper, we are interested in generating fine-grained cartoon faces for various groups.

63, TITLE:      Research Progress of Convolutional Neural Network and its Application in Object Detection
http://arxiv.org/abs/2007.13284
AUTHORS:        Wei Zhang ; Zuoxiang Zeng
COMMENTS:       11 pages, journal paper
HIGHLIGHT:      This paper summarizes the research progress of convolutional neural networks and their applications in object detection, and focuses on analyzing and discussing a specific idea and method of applying convolutional neural networks for object detection, pointing out the current deficiencies and future development direction.

64, TITLE:      NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets
http://arxiv.org/abs/2007.13339
AUTHORS:        Hamada A. Nayel
COMMENTS:       Working notes of NAYEL's team submission to task 12 at SemEval-2020
HIGHLIGHT:      In this paper, we present the system submitted to "SemEval-2020 Task 12".

65, TITLE:      K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations
http://arxiv.org/abs/2007.13310
AUTHORS:        Haohang Xu ; Hongkai Xiong ; Guo-Jun Qi
HIGHLIGHT:      In this paper, we propose the $K$-Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual instances.

66, TITLE:      Split Computing for Complex Object Detectors: Challenges and Preliminary Results
http://arxiv.org/abs/2007.13312
AUTHORS:        Yoshitomo Matsubara ; Marco Levorato
COMMENTS:       Accepted to EMDL '20 (4th International Workshop on Embedded and Mobile Deep Learning) co-located with ACM MobiCom 2020
HIGHLIGHT:      In this paper, we discuss the challenges in developing split computing methods for powerful R-CNN object detectors trained on a large dataset, COCO 2017.

67, TITLE:      Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches
http://arxiv.org/abs/2007.13314
AUTHORS:        Zhi Chen ; Sen Wang ; Jingjing Li ; Zi Huang
COMMENTS:       ACM MM 2020
HIGHLIGHT:      To address these issues, we propose a novel framework called multi-patch generative adversarial nets (MPGAN) that synthesises local patch features and labels unseen classes with a novel weighted voting strategy.

68, TITLE:      KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media
http://arxiv.org/abs/2007.13184
AUTHORS:        Ali Safaya ; Moutasem Abdullatif ; Deniz Yuret
COMMENTS:       to be published in the proceedings of the 14th International Workshop on Semantic Evaluation (SemEval2020), Association for Computational Linguistics (ACL)
HIGHLIGHT:      In this paper, we describe our approach to utilize pre-trained BERT models with Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language Identification shared task (OffensEval 2020), which is a part of the SemEval 2020.

69, TITLE:      Reconstructing NBA Players
http://arxiv.org/abs/2007.13303
AUTHORS:        Luyang Zhu ; Konstantinos Rematas ; Brian Curless ; Steve Seitz ; Ira Kemelmacher-Shlizerman
COMMENTS:       ECCV 2020
HIGHLIGHT:      Based on these models, we introduce a new method that takes as input a single photo of a clothed player in any basketball pose and outputs a high resolution mesh and 3D pose for that player.

70, TITLE:      Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language Model
http://arxiv.org/abs/2007.13306
AUTHORS:        Serena Y. Kim ; Koushik Ganesan ; Princess Dickens ; Soumya Panda
HIGHLIGHT:      This paper examines public sentiment toward solar energy in the United States using data from Twitter, a micro-blogging platform in which people post messages, known as tweets.

71, TITLE:      Learning and aggregating deep local descriptors for instance-level recognition
http://arxiv.org/abs/2007.13172
AUTHORS:        Giorgos Tolias ; Tomas Jenicek ; Ondřej Chum
COMMENTS:       ECCV 2020
HIGHLIGHT:      We propose an efficient method to learn deep local descriptors for instance-level recognition.

72, TITLE:      Deep Photometric Stereo for Non-Lambertian Surfaces
http://arxiv.org/abs/2007.13145
AUTHORS:        Guanying Chen ; Kai Han ; Boxin Shi ; Yasuyuki Matsushita ; Kwan-Yee K. Wong
HIGHLIGHT:      To deal with the uncalibrated scenario where light directions are unknown, we introduce a new convolutional network, named LCNet, to estimate light directions from input images.

73, TITLE:      Virtual Multi-view Fusion for 3D Semantic Segmentation
http://arxiv.org/abs/2007.13138
AUTHORS:        Abhijit Kundu ; Xiaoqi Yin ; Alireza Fathi ; David Ross ; Brian Brewington ; Thomas Funkhouser ; Caroline Pantofaru
COMMENTS:       To appear in ECCV 2020
HIGHLIGHT:      In this paper we revisit the classic multiview representation of 3D meshes and study several techniques that make them effective for 3D semantic segmentation of meshes.

74, TITLE:      Contrastive Visual-Linguistic Pretraining
http://arxiv.org/abs/2007.13135
AUTHORS:        Lei Shi ; Kai Shuang ; Shijie Geng ; Peng Su ; Zhengkai Jiang ; Peng Gao ; Zuohui Fu ; Gerard de Melo ; Sen Su
HIGHLIGHT:      To overcome these issues, we propose unbiased Contrastive Visual-Linguistic Pretraining (CVLP), which constructs a visual self-supervised loss built upon contrastive learning.

75, TITLE:      GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision
http://arxiv.org/abs/2007.13124
AUTHORS:        Lei Ke ; Shichao Li ; Yanan Sun ; Yu-Wing Tai ; Chi-Keung Tang
COMMENTS:       ECCV 2020
HIGHLIGHT:      We present a novel end-to-end framework named as GSNet (Geometric and Scene-aware Network), which jointly estimates 6DoF poses and reconstructs detailed 3D car shapes from single urban street view.

76, TITLE:      Challenge-Aware RGBT Tracking
http://arxiv.org/abs/2007.13143
AUTHORS:        Chenglong Li ; Lei Liu ; Andong Lu ; Qing Ji ; Jin Tang
COMMENTS:       Accepted by ECCV 2020
HIGHLIGHT:      In this paper, we propose a novel challenge-aware neural network to handle the modality-shared challenges (e.g., fast motion, scale variation and occlusion) and the modality-specific ones (e.g., illumination variation and thermal crossover) for RGBT tracking.

77, TITLE:      Cyber Threat Intelligence for Secure Smart City
http://arxiv.org/abs/2007.13233
AUTHORS:        Najla Al-Taleb ; Nazar Abbas Saqib ;  Atta-ur-Rahman ; Sujata Dash
HIGHLIGHT:      This work proposes a hybrid deep learning (DL) model for cyber threat intelligence (CTI) to improve threats classification performance based on convolutional neural network (CNN) and quasi-recurrent neural network (QRNN).

78, TITLE:      Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction
http://arxiv.org/abs/2007.13224
AUTHORS:        Hasib Zunair ; Aimon Rahman ; Nabeel Mohammed ; Joseph Paul Cohen
COMMENTS:       Accepted for publication at the MICCAI 2020 International Workshop on PRedictive Intelligence In MEdicine (PRIME)
HIGHLIGHT:      A common approach to medical image analysis on volumetric data uses deep 2D convolutional neural networks (CNNs).

79, TITLE:      Computing zeta functions of large polynomial systems over finite fields
http://arxiv.org/abs/2007.13214
AUTHORS:        Qi Cheng ; J. Maurice Rojas ; Daqing Wan
HIGHLIGHT:      In this paper, we improve the algorithms of Lauder-Wan \cite{LW} and Harvey \cite{Ha} to compute the zeta function of a system of $m$ polynomial equations in $n$ variables over the finite field $\FF_q$ of $q$ elements, for $m$ large.

80, TITLE:      Building Trust in Autonomous Vehicles: Role of Virtual Reality Driving Simulators in HMI Design
http://arxiv.org/abs/2007.13371
AUTHORS:        Lia Morra ; Fabrizio Lamberti ; F. Gabriele Pratticó ; Salvatore La Rosa ; Paolo Montuschi
HIGHLIGHT:      In this work, we propose a methodology to validate the user experience in AVs based on continuous, objective information gathered from physiological signals, while the user is immersed in a Virtual Reality-based driving simulation.

81, TITLE:      Part-Aware Data Augmentation for 3D Object Detection in Point Cloud
http://arxiv.org/abs/2007.13373
AUTHORS:        Jaeseok Choi ; Yeji Song ; Nojun Kwak
HIGHLIGHT:      In this paper, we propose part-aware data augmentation (PA-AUG) that can better utilize rich information of 3D label to enhance the performance of 3D object detectors.

82, TITLE:      Decomposed Generation Networks with Structure Prediction for Recipe Generation from Food Images
http://arxiv.org/abs/2007.13374
AUTHORS:        Hao Wang ; Guosheng Lin ; Steven C. H. Hoi ; Chunyan Miao
HIGHLIGHT:      To help the model capture the recipe structure and avoid missing some cooking details, we propose a novel framework: Decomposed Generation Networks (DGN) with structure prediction, to get more structured and complete recipe generation outputs.

83, TITLE:      OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
http://arxiv.org/abs/2007.13215
AUTHORS:        Weifeng Chen ; Shengyi Qian ; David Fan ; Noriyuki Kojima ; Max Hamilton ; Jia Deng
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We address this issue by presenting Open Annotations of Single Image Surfaces (OASIS), a dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images.

84, TITLE:      FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers & Goods Transportation
http://arxiv.org/abs/2007.13699
AUTHORS:        Kaushik Manchella ; Abhishek K. Umrawal ; Vaneet Aggarwal
HIGHLIGHT:      We propose FlexPool, a distributed model-free deep reinforcement learning algorithm that jointly serves passengers & goods workloads by learning optimal dispatch policies from its interaction with the environment.

85, TITLE:      IdSan: An identity-based memory sanitizer for fuzzing binaries
http://arxiv.org/abs/2007.13113
AUTHORS:        Jos Craaijo
HIGHLIGHT:      In this paper we introduce an identity-based memory sanitizer for binary AArch64 programs which does not need access to the source code.

86, TITLE:      SADet: Learning An Efficient and Accurate Pedestrian Detector
http://arxiv.org/abs/2007.13119
AUTHORS:        Chubin Zhuang ; Zhen Lei ; Stan Z. Li
HIGHLIGHT:      To this end, this paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector, forming a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection, which includes three main improvements.

87, TITLE:      UIAI System for Short-Duration Speaker Verification Challenge 2020
http://arxiv.org/abs/2007.13118
AUTHORS:        Md Sahidullah ; Achintya Kumar Sarkar ; Ville Vestman ; Xuechen Liu ; Romain Serizel ; Tomi Kinnunen ; Zheng-Hua Tan ; Emmanuel Vincent
HIGHLIGHT:      In this work, we present the system description of the UIAI entry for the short-duration speaker verification (SdSV) challenge 2020.

88, TITLE:      Towards End-to-end Video-based Eye-Tracking
http://arxiv.org/abs/2007.13120
AUTHORS:        Seonwook Park ; Emre Aksan ; Xucong Zhang ; Otmar Hilliges
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      In response to this understanding, we propose a novel dataset and accompanying method which aims to explicitly learn these semantic and temporal relationships.

89, TITLE:      CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
http://arxiv.org/abs/2007.13202
AUTHORS:        Rohan Chitnis ; Tom Silver ; Beomjoon Kim ; Leslie Pack Kaelbling ; Tomas Lozano-Perez
HIGHLIGHT:      We observe that (1) imposing a constraint can induce context-specific independences that render some aspects of the domain irrelevant, and (2) an agent can take advantage of this fact by imposing constraints on its own behavior.

90, TITLE:      Regularized Flexible Activation Function Combinations for Deep Neural Networks
http://arxiv.org/abs/2007.13101
AUTHORS:        Renlong Jie ; Junbin Gao ; Andrey Vasnev ; Min-ngoc Tran
HIGHLIGHT:      In this study, three principles of choosing flexible activation components are proposed and a general combined form of flexible activation functions is implemented.

91, TITLE:      A Closer Look at Art Mediums: The MAMe Image Classification Dataset
http://arxiv.org/abs/2007.13693
AUTHORS:        Ferran Parés ; Anna Arias-Duart ; Dario Garcia-Gasulla ; Gema Campo-Francés ; Nina Viladrich ; Eduard Ayguadé ; Jesús Labarta
HIGHLIGHT:      To challenge the AI community, this work introduces a novel image classification task focused on museum art mediums, the MAMe dataset.

92, TITLE:      Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning
http://arxiv.org/abs/2007.13106
AUTHORS:        Sohaib Younis ; Marco Schmidt ; Claus Weiland ; Steffan Dressler ; Bernhard Seeger ; Thomas Hickler
HIGHLIGHT:      As herbarium specimens are increasingly becoming digitized and accessible in online repositories, advanced computer vision techniques are being used to extract information from them.

93, TITLE:      Ordinary Differential Equation and Complex Matrix Exponential for Multi-resolution Image Registration
http://arxiv.org/abs/2007.13683
AUTHORS:        Abhishek Nan ; Matthew Tennant ; Uriel Rubin ; Nilanjan Ray
COMMENTS:       Software: https://github.com/abnan/ODECME
HIGHLIGHT:      In this work, we emphasize on using complex matrix exponential (CME) over real matrix exponential to compute transformation matrices.

94, TITLE:      The Complexity of the Distributed Constraint Satisfaction Problem
http://arxiv.org/abs/2007.13594
AUTHORS:        Silvia Butti ; Victor Dalmau
COMMENTS:       Submitted to SODA'21
HIGHLIGHT:      We study the complexity of the Distributed Constraint Satisfaction Problem (DCSP) on a synchronous, anonymous network from a theoretical standpoint.

95, TITLE:      A Conversational Digital Assistant for Intelligent Process Automation
http://arxiv.org/abs/2007.13256
AUTHORS:        Yara Rizk ; Vatche Isahagian ; Scott Boag ; Yasaman Khazaeni ; Merve Unuvar ; Vinod Muthusamy ; Rania Khalaf
COMMENTS:       International Conference on Business Process Management 2020 RPA Forum
HIGHLIGHT:      In this work, we explore interactive automation in the form of a conversational digital assistant.

96, TITLE:      From Robotic Process Automation to Intelligent Process Automation: Emerging Trends
http://arxiv.org/abs/2007.13257
AUTHORS:        Tathagata Chakraborti ; Vatche Isahagian ; Rania Khalaf ; Yasaman Khazaeni ; Vinod Muthusamy ; Yara Rizk ; Merve Unuvar
COMMENTS:       Internation Conference on Business Process Management 2020 RPA Forum
HIGHLIGHT:      In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes.

97, TITLE:      REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question Answering
http://arxiv.org/abs/2007.13262
AUTHORS:        Siwen Luo ; Soyeon Caren Han ; Kaiyuan Sun ; Josiah Poon
COMMENTS:       13 pages, 3 figures
HIGHLIGHT:      In this paper, we propose a deep reasoning VQA model with explicit visual structure-aware textual information, and it works well in capturing step-by-step reasoning process and detecting a complex object-relationship in photo-realistic images.

98, TITLE:      Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation
http://arxiv.org/abs/2007.13264
AUTHORS:        Pingyang Dai ; Peixian Chen ; Qiong Wu ; Xiaopeng Hong ; Qixiang Ye ; Qi Tian ; Rongrong Ji
COMMENTS:       9 pages, 6 figures
HIGHLIGHT:      In this paper, we break the concept of task-orientation into task-relevance and task-irrelevance, and propose a dynamic task-oriented disentangling network (DTDN) to learn disentangled representations in an end-to-end fashion for UDA.

99, TITLE:      Dual Distribution Alignment Network for Generalizable Person Re-Identification
http://arxiv.org/abs/2007.13249
AUTHORS:        Peixian Chen ; Pingyang Dai ; Jianzhuang Liu ; Feng Zheng ; Qi Tian ; Rongrong Ji
COMMENTS:       8 pages, 3 figures
HIGHLIGHT:      In this paper, we present a Dual Distribution Alignment Network (DDAN), which handles this challenge by mapping images into a domain-invariant feature space by selectively aligning distributions of multiple source domains.

100, TITLE:     Point-to-set distance functions for weakly supervised segmentation
http://arxiv.org/abs/2007.13251
AUTHORS:        Bas Peters
HIGHLIGHT:      We present a new algorithm to include such information via constraints on the network output, implemented via projection-based point-to-set distance functions.

101, TITLE:     Benchmarking Meta-heuristic Optimization
http://arxiv.org/abs/2007.13476
AUTHORS:        Mona Nasr ; Omar Farouk ; Ahmed Mohamedeen ; Ali Elrafie ; Marwan Bedeir ; Ali Khaled
COMMENTS:       International Journal of Advanced Networking and Applications - IJANA
HIGHLIGHT:      They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm's result is close to the optimal result.

102, TITLE:     Identity-Guided Human Semantic Parsing for Person Re-Identification
http://arxiv.org/abs/2007.13467
AUTHORS:        Kuan Zhu ; Haiyun Guo ; Zhiwei Liu ; Ming Tang ; Jinqiao Wang
COMMENTS:       Accepted by ECCV 2020 spotlight
HIGHLIGHT:      In this paper, we propose the identity-guided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person re-ID only with person identity labels.

103, TITLE:     3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
http://arxiv.org/abs/2007.13666
AUTHORS:        Xiangyu Xu ; Hao Chen ; Francesc Moreno-Noguer ; Laszlo A. Jeni ; Fernando De la Torre
COMMENTS:       ECCV 2020, project page: https://sites.google.com/view/xiangyuxu/3d_eccv20
HIGHLIGHT:      To address the above issues, this paper proposes a novel algorithm called RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme.

104, TITLE:     Towards Learning Convolutions from Scratch
http://arxiv.org/abs/2007.13657
AUTHORS:        Behnam Neyshabur
COMMENTS:       18 pages, 9 figures, 4 tables
HIGHLIGHT:      To find architectures with small description length, we propose $\beta$-LASSO, a simple variant of LASSO algorithm that, when applied on fully-connected networks for image classification tasks, learns architectures with local connections and achieves state-of-the-art accuracies for training fully-connected nets on CIFAR-10 (85.19%), CIFAR-100 (59.56%) and SVHN (94.07%) bridging the gap between fully-connected and convolutional nets.

105, TITLE:     Evaluating the reliability of acoustic speech embeddings
http://arxiv.org/abs/2007.13542
AUTHORS:        Robin Algayres ; Mohamed Salah Zaiem ; Benoit Sagot ; Emmanuel Dupoux
COMMENTS:       Conference paper at Interspeech 2020
HIGHLIGHT:      This makes it unrealistic at present to propose a task-independent silver bullet method for computing the intrinsic quality of speech embeddings.

106, TITLE:     Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser
http://arxiv.org/abs/2007.13640
AUTHORS:        Zahra Kadkhodaie ; Eero P. Simoncelli
COMMENTS:       15 pages, 13 figures
HIGHLIGHT:      We demonstrate this general form of transfer learning in multiple applications, using the same algorithm to produce high-quality solutions for deblurring, super-resolution, inpainting, and compressive sensing.

107, TITLE:     Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
http://arxiv.org/abs/2007.13544
AUTHORS:        Noam Brown ; Anton Bakhtin ; Adam Lerer ; Qucheng Gong
HIGHLIGHT:      This paper presents ReBeL, a general framework for self-play reinforcement learning and search for imperfect-information games.

108, TITLE:     Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification
http://arxiv.org/abs/2007.13547
AUTHORS:        Changsheng Li ; Chong Liu ; Lixin Duan ; Peng Gao ; Kai Zheng
COMMENTS:       Accepted by IEEE TNNLS
HIGHLIGHT:      In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem.

109, TITLE:     Differentiable Manifold Reconstruction for Point Cloud Denoising
http://arxiv.org/abs/2007.13551
AUTHORS:        Shitong Luo ; Wei Hu
COMMENTS:       This work has been accepted to ACM MM 2020
HIGHLIGHT:      To this end, we propose to learn the underlying manifold of a noisy point cloud from differentiably subsampled points with trivial noise perturbation and their embedded neighborhood feature, aiming to capture intrinsic structures in point clouds.

110, TITLE:     Message Passing Least Squares Framework and its Application to Rotation Synchronization
http://arxiv.org/abs/2007.13638
AUTHORS:        Yunpeng Shi ; Gilad Lerman
COMMENTS:       To appear in ICML 2020
HIGHLIGHT:      We demonstrate the superior performance of our algorithm over state-of-the-art methods for rotation synchronization using both synthetic and real data.

111, TITLE:     Score-Based Explanations in Data Management and Machine Learning
http://arxiv.org/abs/2007.12799
AUTHORS:        Leopoldo Bertossi
COMMENTS:       Companion paper for an upcoming conference tutorial
HIGHLIGHT:      We describe some approaches to explanations for observed outcomes in data management and machine learning.

112, TITLE:     Black-Box Face Recovery from Identity Features
http://arxiv.org/abs/2007.13635
AUTHORS:        Anton Razzhigaev ; Klim Kireev ; Edgar Kaziakhmedov ; Nurislam Tursynbek ; Aleksandr Petyushko
HIGHLIGHT:      In this work, we present a novel algorithm based on an it-erative sampling of random Gaussian blobs for black-box face recovery,given only an output feature vector of deep face recognition systems.

113, TITLE:     Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing
http://arxiv.org/abs/2007.13632
AUTHORS:        Yi Zhang ; Jitao Sang
HIGHLIGHT:      Specifically, to ensure the adversarial generalization as well as cross-task transferability, we propose to couple the operations of target task classifier training, bias task classifier training, and adversarial example generation.

114, TITLE:     The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study
http://arxiv.org/abs/2007.13521
AUTHORS:        Naser Damer ; Jonas Henry Grebe ; Cong Chen ; Fadi Boutros ; Florian Kirchbuchner ; Arjan Kuijper
COMMENTS:       Accepted at BIOSIG2020
HIGHLIGHT:      We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases.

115, TITLE:     A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records
http://arxiv.org/abs/2007.12780
AUTHORS:        Parthasarathy Suryanarayanan ; Bhavani Iyer ; Prithwish Chakraborty ; Bibo Hao ; Italo Buleje ; Piyush Madan ; James Codella ; Antonio Foncubierta ; Divya Pathak ; Sarah Miller ; Amol Rajmane ; Shannon Harrer ; Gigi Yuan-Reed ; Daby Sow
HIGHLIGHT:      In this paper, we propose a novel canonical architecture for the development of AI models in healthcare that addresses these challenges.

116, TITLE:     Two-Level Residual Distillation based Triple Network for Incremental Object Detection
http://arxiv.org/abs/2007.13428
AUTHORS:        Dongbao Yang ; Yu Zhou ; Dayan Wu ; Can Ma ; Fei Yang ; Weiping Wang
HIGHLIGHT:      In this paper, we propose a novel incremental object detector based on Faster R-CNN to continuously learn from new object classes without using old data.

117, TITLE:     Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships
http://arxiv.org/abs/2007.13417
AUTHORS:        Wufei Ma ; Elizabeth Kautz ; Arun Baskaran ; Aritra Chowdhury ; Vineet Joshi ; Bülent Yener ; Daniel Lewis
COMMENTS:       14 pages, 15 figures
HIGHLIGHT:      A binary alloy (uranium-molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions.

118, TITLE:     Hyper-local sustainable assortment planning
http://arxiv.org/abs/2007.13414
AUTHORS:        Nupur Aggarwal ; Abhishek Bansal ; Kushagra Manglik ; Kedar Kulkarni ; Vikas Raykar
HIGHLIGHT:      Assortment planning, an important seasonal activity for any retailer, involves choosing the right subset of products to stock in each store.While existing approaches only maximize the expected revenue, we propose including the environmental impact too, through the Higg Material Sustainability Index.

119, TITLE:     Contraction Mapping of Feature Norms for Classifier Learning on the Data with Different Quality
http://arxiv.org/abs/2007.13406
AUTHORS:        Weihua Liu ; Xiabi Liu ; Murong Wang ; Ling Ma ; Yunde Jia
HIGHLIGHT:      In this paper, we discover the positive correlation between the feature norm of an image and its quality through careful ex-periments on various applications and various deep neural networks.

120, TITLE:     XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
http://arxiv.org/abs/2007.13408
AUTHORS:        Sina Amirrajab ; Samaneh Abbasi-Sureshjani ; Yasmina Al Khalil ; Cristian Lorenz ; Juergen Weese ; Josien Pluim ; Marcel Breeuwer
COMMENTS:       Accepted for MICCAI 2020
HIGHLIGHT:      We propose a novel method for synthesizing cardiac magnetic resonance (CMR) images on a population of virtual subjects with a large anatomical variation, introduced using the 4D eXtended Cardiac and Torso (XCAT) computerized human phantom.

121, TITLE:     YOLOpeds: Efficient Real-Time Single-Shot Pedestrian Detection for Smart Camera Applications
http://arxiv.org/abs/2007.13404
AUTHORS:        Christos Kyrkou
HIGHLIGHT:      In particular, the contributions of this work are the following: 1) An efficient backbone combining multi-scale feature operations, 2) a more elaborate loss function for improved localization, 3) an anchor-less approach for detection, The proposed approach called YOLOpeds is evaluated using the PETS2009 surveillance dataset on 320x320 images.

122, TITLE:     Information Fusion on Belief Networks
http://arxiv.org/abs/2007.12989
AUTHORS:        Shawn C. Eastwood ; Svetlana N. Yanushkevich
COMMENTS:       25 pages, pages of Appendix, 3 figures
HIGHLIGHT:      An objective requirement for information fusion algorithms is provided, and is satisfied by all models of fusion presented in this paper.

123, TITLE:     Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter
http://arxiv.org/abs/2007.12988
AUTHORS:        Thayer Alshaabi ; Jane L. Adams ; Michael V. Arnold ; Joshua R. Minot ; David R. Dewhurst ; Andrew J. Reagan ; Christopher M. Danforth ; Peter Sheridan Dodds
COMMENTS:       Main text: 9 pages, 4 figures; Supplementary text: 11 pages, 8 figures, 4 tables. Website: https://storywrangling.org/ Code: https://gitlab.com/compstorylab/storywrangler
HIGHLIGHT:      Here, we describe Storywrangler, an ongoing, day-scale curation of over 100 billion tweets containing around 1 trillion 1-grams from 2008 to 2020.

124, TITLE:     Coupled Relational Symbolic Execution for Differential Privacy
http://arxiv.org/abs/2007.12987
AUTHORS:        Gian Pietro Farina ; Stephen Chong ; Marco Gaboardi
HIGHLIGHT:      In this work we propose a technique based on symbolic execution for reasoning about differential privacy.

125, TITLE:     GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor
http://arxiv.org/abs/2007.12979
AUTHORS:        Lingjing Wang ; Xiang Li ; Yi Fang
HIGHLIGHT:      In this paper, we propose a novel method named GP-Aligner to deal with the problem of non-rigid groupwise point set registration.

126, TITLE:     3D Neural Network for Lung Cancer Risk Prediction on CT Volumes
http://arxiv.org/abs/2007.12898
AUTHORS:        Daniel Korat
HIGHLIGHT:      In this report, we reproduce a state-of-the-art deep learning algorithm for lung cancer risk prediction.

127, TITLE:     MirrorNet: Bio-Inspired Adversarial Attack for Camouflaged Object Segmentation
http://arxiv.org/abs/2007.12881
AUTHORS:        Jinnan Yan ; Trung-Nghia Le ; Khanh-Duy Nguyen ; Minh-Triet Tran ; Thanh-Toan Do ; Tam V. Nguyen
COMMENTS:       Accept with Minor Revision to Pattern Recognition Journal
HIGHLIGHT:      In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and adversarial attack for the camouflaged object segmentation.

128, TITLE:     Learning Lane Graph Representations for Motion Forecasting
http://arxiv.org/abs/2007.13732
AUTHORS:        Ming Liang ; Bin Yang ; Rui Hu ; Yun Chen ; Renjie Liao ; Song Feng ; Raquel Urtasun
COMMENTS:       ECCV 2020 Oral
HIGHLIGHT:      We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions.

129, TITLE:     Learning Disentangled Representations with Latent Variation Predictability
http://arxiv.org/abs/2007.12885
AUTHORS:        Xinqi Zhu ; Chang Xu ; Dacheng Tao
COMMENTS:       14 pages, ECCV20
HIGHLIGHT:      This paper defines the variation predictability of latent disentangled representations.

130, TITLE:     Approximated Bilinear Modules for Temporal Modeling
http://arxiv.org/abs/2007.12887
AUTHORS:        Xinqi Zhu ; Chang Xu ; Langwen Hui ; Cewu Lu ; Dacheng Tao
COMMENTS:       8 pages, ICCV19
HIGHLIGHT:      Besides, we introduce snippet sampling and shifting inference to boost sparse-frame video classification performance.

131, TITLE:     Associative3D: Volumetric Reconstruction from Sparse Views
http://arxiv.org/abs/2007.13727
AUTHORS:        Shengyi Qian ; Linyi Jin ; David F. Fouhey
COMMENTS:       ECCV 2020
HIGHLIGHT:      We propose a new approach that estimates reconstructions, distributions over the camera/object and camera/camera transformations, as well as an inter-view object affinity matrix.

132, TITLE:     Noisy Agents: Self-supervised Exploration by Predicting Auditory Events
http://arxiv.org/abs/2007.13729
AUTHORS:        Chuang Gan ; Xiaoyu Chen ; Phillip Isola ; Antonio Torralba ; Joshua B. Tenenbaum
COMMENTS:       Project page: http://noisy-agent.csail.mit.edu
HIGHLIGHT:      In this work, we propose a novel type of intrinsic motivation for Reinforcement Learning (RL) that encourages the agent to understand the causal effect of its actions through auditory event prediction.

133, TITLE:     Three-stage intelligent support of clinical decision making for higher trust, validity, and explainability
http://arxiv.org/abs/2007.12870
AUTHORS:        Sergey V. Kovalchuk ; Georgy D. Kopanitsa ; Ilia V. Derevitskii ; Daria A. Savitskaya
HIGHLIGHT:      The paper presents the approach for the building of consistent and applicable clinical decision support systems (CDSS) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSS in real-world applications.

134, TITLE:     OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
http://arxiv.org/abs/2007.12868
AUTHORS:        Zhengqin Li ; Ting-Wei Yu ; Shen Sang ; Sarah Wang ; Sai Bi ; Zexiang Xu ; Hong-Xing Yu ; Kalyan Sunkavalli ; Miloš Hašan ; Ravi Ramamoorthi ; Manmohan Chandraker
HIGHLIGHT:      We aim to make the dataset creation process for indoor scenes widely accessible, allowing researchers to transform casually acquired scans to large-scale datasets with high-quality ground truth.

135, TITLE:     Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation
http://arxiv.org/abs/2007.13715
AUTHORS:        Kenzo Lobos-Tsunekawa ; Tatsuya Harada
COMMENTS:       Accepted to IROS'2020
HIGHLIGHT:      We propose a method that learns on an observation space constructed by point clouds and environment randomization, generalizing among robots and simulators to achieve sim-to-real, while also addressing partial observability.

136, TITLE:     Applying Semantic Segmentation to Autonomous Cars in the Snowy Environment
http://arxiv.org/abs/2007.12869
AUTHORS:        Zhaoyu Pan ; Takanori Emaru ; Ankit Ravankar ; Yukinori Kobayashi
COMMENTS:       4 pages, 5 Figures
HIGHLIGHT:      We train the Fully Convolutional Networks (FCN) on our own dataset and present the experimental results.

137, TITLE:     The Unsupervised Method of Vessel Movement Trajectory Prediction
http://arxiv.org/abs/2007.13712
AUTHORS:        Chih-Wei Chen ; Charles Harrison ; Hsin-Hsiung Huang
HIGHLIGHT:      This article presents an unsupervised method of ship movement trajectory prediction which represents the data in a three-dimensional space which consists of time difference between points, the scaled error distance between the tested and its predicted forward and backward locations, and the space-time angle.

138, TITLE:     Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning
http://arxiv.org/abs/2007.12764
AUTHORS:        Ghazale Ghorbanzade ; Zahra Nabizadeh-ShahreBabak ; Shadrokh Samavi ; Nader Karimi ; Ali Emami ; Pejman Khadivi
COMMENTS:       5 pages 2 figures
HIGHLIGHT:      We are proposing a deep learning-based method for selecting an informative subset of channels that produce high classification accuracy.

139, TITLE:     BabyAI 1.1
http://arxiv.org/abs/2007.12770
AUTHORS:        David Yu-Tung Hui ; Maxime Chevalier-Boisvert ; Dzmitry Bahdanau ; Yoshua Bengio
COMMENTS:       9 pages, 1 figure, technical report
HIGHLIGHT:      BabyAI 1.1

140, TITLE:     Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
http://arxiv.org/abs/2007.12750
AUTHORS:        Michael Cogswell ; Jiasen Lu ; Rishabh Jain ; Stefan Lee ; Devi Parikh ; Dhruv Batra
COMMENTS:       19 pages, 8 figures
HIGHLIGHT:      In this work, we study a setting we call "Dialog without Dialog", which requires agents to develop visually grounded dialog models that can adapt to new tasks without language level supervision.

141, TITLE:     Hard negative examples are hard, but useful
http://arxiv.org/abs/2007.12749
AUTHORS:        Hong Xuan ; Abby Stylianou ; Xiaotong Liu ; Robert Pless
COMMENTS:       CV, Triplet loss, Image embedding, 14 pages, 9 figures, ECCV 2020
HIGHLIGHT:      In this paper, we characterize the space of triplets and derive why hard negatives make triplet loss training fail.

142, TITLE:     Consistent Transcription and Translation of Speech
http://arxiv.org/abs/2007.12741
AUTHORS:        Matthias Sperber ; Hendra Setiawan ; Christian Gollan ; Udhyakumar Nallasamy ; Matthias Paulik
COMMENTS:       Accepted at TACL (pre-MIT Press publication version)
HIGHLIGHT:      We introduce a methodology to evaluate consistency and compare several modeling approaches, including the traditional cascaded approach and end-to-end models.



==========Updates to Previous Papers==========
1, TITLE:       Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery
http://arxiv.org/abs/2007.09107
AUTHORS:        Emanuele Colleoni ; Philip Edwards ; Danail Stoyanov
HIGHLIGHT:      At present labelled data for training deep learning models is still lacking for semantic surgical instrument segmentation and in this paper we show that it may be possible to use robot kinematic data coupled with laparoscopic images to alleviate the labelling problem.

2, TITLE:       Prediction error-driven memory consolidation for continual learning. On the case of adaptive greenhouse models
http://arxiv.org/abs/2006.12616
AUTHORS:        Guido Schillaci ; Luis Miranda ; Uwe Schmidt
COMMENTS:       Revised version. Paper under review, submitted to Springer German Journal on Artificial Intelligence (K\"unstliche Intelligenz), Special Issue on Developmental Robotics
HIGHLIGHT:      This work presents a model trained on data recorded from research facilities and transferred to a production greenhouse.

3, TITLE:       3D Point Cloud Descriptors in Hand-crafted and Deep Learning Age: State-of-the-Art
http://arxiv.org/abs/1802.02297
AUTHORS:        Xian-Feng Han ; Shi-Jie Sun ; Xiang-Yu Song ; Guo-Qiang Xiao
HIGHLIGHT:      In this paper, we give a comprehensively insightful investigation of the existing 3D point cloud descriptors.

4, TITLE:       Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation
http://arxiv.org/abs/2004.04398
AUTHORS:        Da Li ; Timothy Hospedales
COMMENTS:       ECCV 2020 CR version
HIGHLIGHT:      In this paper we take an orthogonal perspective and propose a framework to further enhance performance by meta-learning the initial conditions of existing DA algorithms.

5, TITLE:       Advances of Transformer-Based Models for News Headline Generation
http://arxiv.org/abs/2007.05044
AUTHORS:        Alexey Bukhtiyarov ; Ilya Gusev
COMMENTS:       Version 2; Accepted to AINL 2020
HIGHLIGHT:      In this paper, we fine-tune two pretrained Transformer-based models (mBART and BertSumAbs) for that task and achieve new state-of-the-art results on the RIA and Lenta datasets of Russian news.

6, TITLE:       EfficientDet: Scalable and Efficient Object Detection
http://arxiv.org/abs/1911.09070
AUTHORS:        Mingxing Tan ; Ruoming Pang ; Quoc V. Le
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency.

7, TITLE:       AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings
http://arxiv.org/abs/2005.13402
AUTHORS:        Pratik Mazumder ; Pravendra Singh ; Kranti Kumar Parida ; Vinay P. Namboodiri
HIGHLIGHT:      In this paper, we solve for the problem of generalized zero-shot learning in a multi-modal setting, where we have novel classes of audio/video during testing that were not seen during training.

8, TITLE:       Explaining an image classifier's decisions using generative models
http://arxiv.org/abs/1910.04256
AUTHORS:        Chirag Agarwal ; Anh Nguyen
COMMENTS:       Preprint. Submission under review
HIGHLIGHT:      Instead, we propose to integrate a generative inpainter into three representative attribution methods to remove an input feature.

9, TITLE:       SpeedNet: Learning the Speediness in Videos
http://arxiv.org/abs/2004.06130
AUTHORS:        Sagie Benaim ; Ariel Ephrat ; Oran Lang ; Inbar Mosseri ; William T. Freeman ; Michael Rubinstein ; Michal Irani ; Tali Dekel
COMMENTS:       Accepted to CVPR 2020 (oral). Project webpage: http://speednet-cvpr20.github.io
HIGHLIGHT:      We show how this single, binary classification network can be used to detect arbitrary rates of speediness of objects.

10, TITLE:      Scalene: Scripting-Language Aware Profiling for Python
http://arxiv.org/abs/2006.03879
AUTHORS:        Emery D. Berger
HIGHLIGHT:      This paper introduces scripting-language aware profiling, and presents Scalene, an implementation of scripting-language aware profiling for Python.

11, TITLE:      NAMF: A Non-local Adaptive Mean Filter for Salt-and-Pepper Noise Removal
http://arxiv.org/abs/1910.07787
AUTHORS:        Houwang Zhang ; Yuan Zhu ; Hanying Zheng
HIGHLIGHT:      In this paper, a novel algorithm called a non-local adaptive mean filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented.

12, TITLE:      ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition
http://arxiv.org/abs/1912.04023
AUTHORS:        Anil S. Baslamisli ; Partha Das ; Hoang-An Le ; Sezer Karaoglu ; Theo Gevers
COMMENTS:       Submitted to International Journal of Computer Vision (IJCV)
HIGHLIGHT:      Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents.

13, TITLE:      Fast Commutative Matrix Algorithm
http://arxiv.org/abs/1904.07683
AUTHORS:        Andreas Rosowski
HIGHLIGHT:      We generalize our result for nx3 and 3x3 matrices and present an algorithm for computing the product of an lxn matrix and an nxm matrix over a commutative ring for odd n using n(lm+l+m-1)/2 multiplications if m is odd and using (n(lm+l+m-1)+l-1)/2 multiplications if m is even.

14, TITLE:      A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
http://arxiv.org/abs/2006.05245
AUTHORS:        Delong Chen ; Shunhui Ji ; Fan Liu1 ; Zewen Li ; Xinyu Zhou
COMMENTS:       under review of PRAI2020
HIGHLIGHT:      In this paper, we present a review of these recently emerging automatic diagnosing models.

15, TITLE:      ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
http://arxiv.org/abs/2003.12181
AUTHORS:        Gopal Sharma ; Difan Liu ; Subhransu Maji ; Evangelos Kalogerakis ; Siddhartha Chaudhuri ; Radomír Měch
HIGHLIGHT:      We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives.

16, TITLE:      A Deductive Verification Framework for Circuit-building Quantum Programs
http://arxiv.org/abs/2003.05841
AUTHORS:        Christophe Chareton ; Sébastien Bardin ; François Bobot ; Valentin Perrelle ; Benoit Valiron
HIGHLIGHT:      We propose Qbricks, the first formal verification environment for circuit-building quantum programs, featuring clear separation between code and proof, parametric specifications and proofs, high degree of proof automation and allowing to encode quantum programs in a natural way, i.e. close to textbook style.

17, TITLE:      Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review
http://arxiv.org/abs/2007.10760
AUTHORS:        Yansong Gao ; Bao Gia Doan ; Zhi Zhang ; Siqi Ma ; Jiliang Zhang ; Anmin Fu ; Surya Nepal ; Hyoungshick Kim
COMMENTS:       29 pages, 9 figures, 2 tables
HIGHLIGHT:      This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning.

18, TITLE:      Improving Black-box Adversarial Attacks with a Transfer-based Prior
http://arxiv.org/abs/1906.06919
AUTHORS:        Shuyu Cheng ; Yinpeng Dong ; Tianyu Pang ; Hang Su ; Jun Zhu
COMMENTS:       NeurIPS 2019; Code available at https://github.com/thu-ml/Prior-Guided-RGF
HIGHLIGHT:      To address these problems, we propose a prior-guided random gradient-free (P-RGF) method to improve black-box adversarial attacks, which takes the advantage of a transfer-based prior and the query information simultaneously.

19, TITLE:      Canonical Functions: a proof via topological dynamics
http://arxiv.org/abs/1610.09660
AUTHORS:        Manuel Bodirsky ; Michael Pinsker
COMMENTS:       9 pages
HIGHLIGHT:      Canonical functions are a powerful concept with numerous applications in the study of groups, monoids, and clones on countable structures with Ramsey-type properties.

20, TITLE:      Appearance-Preserving 3D Convolution for Video-based Person Re-identification
http://arxiv.org/abs/2007.08434
AUTHORS:        Xinqian Gu ; Hong Chang ; Bingpeng Ma ; Hongkai Zhang ; Xilin Chen
COMMENTS:       Accepted by ECCV2020 (Oral)
HIGHLIGHT:      To address this problem, we propose AppearancePreserving 3D Convolution (AP3D), which is composed of two components: an Appearance-Preserving Module (APM) and a 3D convolution kernel.

21, TITLE:      The Electromagnetic Balance Game: A Probabilistic Perspective
http://arxiv.org/abs/2007.10735
AUTHORS:        Fangqi Li
COMMENTS:       13 pages, 5 figures,
HIGHLIGHT:      In this paper some variants of the balance game are dicussed, especially from a probabilistic perspective.

22, TITLE:      DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation
http://arxiv.org/abs/2005.07029
AUTHORS:        Yi-Chen Chen ; Jui-Yang Hsu ; Cheng-Kuang Lee ; Hung-yi Lee
COMMENTS:       Accepted at INTERSPEECH 2020
HIGHLIGHT:      Therefore in this paper, we propose an ASR approach with efficient gradient-based architecture search, DARTS-ASR.

23, TITLE:      Bridging Visual Perception with Contextual Semantics for Understanding Robot Manipulation Tasks
http://arxiv.org/abs/1909.07459
AUTHORS:        Chen Jiang ; Martin Jagersand
HIGHLIGHT:      In this paper, we propose an implementing framework to generate high-level conceptual dynamic knowledge graphs from video clips.

24, TITLE:      Parallel Exploration via Negatively Correlated Search
http://arxiv.org/abs/1910.07151
AUTHORS:        Peng Yang ; Qi Yang ; Ke Tang ; Xin Yao
HIGHLIGHT:      In this paper, a more principled NCS is presented, explaining that the parallel exploration is equivalent to the explicit maximization of both the population diversity and the population solution qualities, and can be optimally obtained by partially gradient descending both models with respect to each search process.

25, TITLE:      The Effect of Moderation on Online Mental Health Conversations
http://arxiv.org/abs/2005.09225
AUTHORS:        David Wadden ; Tal August ; Qisheng Li ; Tim Althoff
COMMENTS:       Accepted as a full paper at ICWSM 2021. 13 pages, 12 figures, 3 tables
HIGHLIGHT:      In this work, we leveraged a natural experiment, occurring across 200,000 messages from 7,000 conversations hosted on a mental health mobile application, to evaluate the effects of moderation on online mental health discussions.

26, TITLE:      Adapting Text Embeddings for Causal Inference
http://arxiv.org/abs/1905.12741
AUTHORS:        Victor Veitch ; Dhanya Sridhar ; David M. Blei
HIGHLIGHT:      This paper develops a method to estimate such causal effects from observational text data, adjusting for confounding features of the text such as the subject or writing quality.

27, TITLE:      The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation
http://arxiv.org/abs/2007.11978
AUTHORS:        Tao Wang ; Yu Li ; Bingyi Kang ; Junnan Li ; Junhao Liew ; Sheng Tang ; Steven Hoi ; Jiashi Feng
COMMENTS:       LVIS 2019 challenge winner, performance significantly improved after challenge submission, accepted at ECCV 2019
HIGHLIGHT:      This work aims to study and address such open challenges.

28, TITLE:      Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
http://arxiv.org/abs/2007.09654
AUTHORS:        Tong Wu ; Qingqiu Huang ; Ziwei Liu ; Yu Wang ; Dahua Lin
COMMENTS:       To appear in ECCV 2020 as a spotlight presentation. Code and models are available at: https://github.com/wutong16/DistributionBalancedLoss
HIGHLIGHT:      We present a new loss function called Distribution-Balanced Loss for the multi-label recognition problems that exhibit long-tailed class distributions.

29, TITLE:      Compact Global Descriptor for Neural Networks
http://arxiv.org/abs/1907.09665
AUTHORS:        Xiangyu He ; Ke Cheng ; Qiang Chen ; Qinghao Hu ; Peisong Wang ; Jian Cheng
COMMENTS:       This paper will be included in our future works as a subsection
HIGHLIGHT:      In this paper, we present a generic family of lightweight global descriptors for modeling the interactions between positions across different dimensions (e.g., channels, frames).

30, TITLE:      Rigging the Lottery: Making All Tickets Winners
http://arxiv.org/abs/1911.11134
AUTHORS:        Utku Evci ; Trevor Gale ; Jacob Menick ; Pablo Samuel Castro ; Erich Elsen
COMMENTS:       Published in Proceedings of the 37th International Conference on Machine Learning. Code can be found in github.com/google-research/rigl
HIGHLIGHT:      In this paper we introduce a method to train sparse neural networks with a fixed parameter count and a fixed computational cost throughout training, without sacrificing accuracy relative to existing dense-to-sparse training methods.

31, TITLE:      A Bounded Measure for Estimating the Benefit of Visualization
http://arxiv.org/abs/2002.05282
AUTHORS:        Min Chen ; Mateu Sbert ; Alfie Abdul-Rahman ; Deborah Silver
COMMENTS:       Comment on version 2: This revised version, which includes a new formal proof, many additions, and a detailed revision report, was submitted to SciVis 2020. Unexpectedly, our revision effort did not have much influence on the SciVis 2020 reviewers who gave an outright rejection with lower scores than EuroVis reviews. We will share these reviews after we have completed our feedback
HIGHLIGHT:      In this work, we propose to revise the existing cost-benefit measure by replacing the unbounded term with a bounded one.

32, TITLE:      Local Search is a Remarkably Strong Baseline for Neural Architecture Search
http://arxiv.org/abs/2004.08996
AUTHORS:        T. Den Ottelander ; A. Dushatskiy ; M. Virgolin ; P. A. N. Bosman
COMMENTS:       20 pages, 15 figures; Added analysis of LS on single-objective NAS tasks in Appendix
HIGHLIGHT:      In this work we consider, for the first time, a simple Local Search (LS) algorithm for NAS.

33, TITLE:      HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples
http://arxiv.org/abs/1909.07558
AUTHORS:        Wanting Yu ; Hongyi Yu ; Lingyun Jiang ; Mengli Zhang ; Kai Qiao ; Linyuan Wang ; Bin Yan
HIGHLIGHT:      In this paper, we propose a defense model to train the classifier into a human-perception classification model with shape preference.

34, TITLE:      Mereosemiotic physicalism: A paradigm for foundational ontologies
http://arxiv.org/abs/2003.11370
AUTHORS:        Martin Thomas Horsch ; Silvia Chiacchiera ; Michael A. Seaton ; Ilian T. Todorov
COMMENTS:       Disclaimer: This work was not carried out within any externally funded project or action. No external funding was used
HIGHLIGHT:      It is explored how foundational ontologies based on physicalist materialism, nominalism, and Peircean semiotics can be applied to represent signs, models of physical systems, and their use in engineering modelling and simulation practice.

35, TITLE:      BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis
http://arxiv.org/abs/1803.08467
AUTHORS:        Zili Yi ; Zhiqin Chen ; Hao Cai ; Wendong Mao ; Minglun Gong ; Hao Zhang
COMMENTS:       26 pages, 20 figures, TIP paper
HIGHLIGHT:      We introduce BSD-GAN, a novel multi-branch and scale-disentangled training method which enables unconditional Generative Adversarial Networks (GANs) to learn image representations at multiple scales, benefiting a wide range of generation and editing tasks.

36, TITLE:      High Performance Sequence-to-Sequence Model for Streaming Speech Recognition
http://arxiv.org/abs/2003.10022
AUTHORS:        Thai-Son Nguyen ; Ngoc-Quan Pham ; Sebastian Stueker ; Alex Waibel
COMMENTS:       To appear in Interspeech 2020
HIGHLIGHT:      In this paper, we propose several techniques to mitigate these problems.

37, TITLE:      Lagrangian Duality in Reinforcement Learning
http://arxiv.org/abs/2007.09998
AUTHORS:        Pranay Pasula
COMMENTS:       8 pages, 0 figures; fixed typo in abstract
HIGHLIGHT:      In this paper, we show how duality is involved in a variety of RL work, from that which spearheaded the field, such as Richard Bellman's value iteration, to that which was done within just the past few years yet has already had significant impact, such as TRPO, A3C, and GAIL.

38, TITLE:      TF-Coder: Program Synthesis for Tensor Manipulations
http://arxiv.org/abs/2003.09040
AUTHORS:        Kensen Shi ; David Bieber ; Rishabh Singh
HIGHLIGHT:      In this work, we present a tool called TF-Coder for programming by example in TensorFlow.

39, TITLE:      An Executable Operational Semantics for Rust with the Formalization of Ownership and Borrowing
http://arxiv.org/abs/1804.07608
AUTHORS:        Shuanglong Kan ; Zhe Chen ; David Sanan ; Shang-Wei Lin ; Yang Liu
HIGHLIGHT:      In this paper, we propose RustSEM, a new executable operational semantics for Rust.

40, TITLE:      Co-evolution of language and agents in referential games
http://arxiv.org/abs/2001.03361
AUTHORS:        Gautier Dagan ; Dieuwke Hupkes ; Elia Bruni
COMMENTS:       10 pages, 9 figures
HIGHLIGHT:      In this work, we first substantiate this claim with a different data set and a wider array of evaluation metrics.

41, TITLE:      Towards Robust Learning with Different Label Noise Distributions
http://arxiv.org/abs/1912.08741
AUTHORS:        Diego Ortego ; Eric Arazo ; Paul Albert ; Noel E. O'Connor ; Kevin McGuinness
HIGHLIGHT:      SSL is then applied twice, once to improve the clean-noisy detection and again for training the final model.

42, TITLE:      SegFix: Model-Agnostic Boundary Refinement for Segmentation
http://arxiv.org/abs/2007.04269
AUTHORS:        Yuhui Yuan ; Jingyi Xie ; Xilin Chen ; Jingdong Wang
COMMENTS:       ECCV 2020. Project Page: https://github.com/openseg-group/openseg.pytorch
HIGHLIGHT:      We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model.

43, TITLE:      Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
http://arxiv.org/abs/2004.06002
AUTHORS:        Hongkai Zhang ; Hong Chang ; Bingpeng Ma ; Naiyan Wang ; Xilin Chen
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      In this work, we first point out the inconsistency problem between the fixed network settings and the dynamic training procedure, which greatly affects the performance.

44, TITLE:      The Decidability of Verification under Promising 2.0
http://arxiv.org/abs/2007.09944
AUTHORS:        Parosh Aziz Abdulla ; Mohamed Faouzi Atig ; Adwait Godbole ; Shankaranarayanan Krishna ; Viktor Vafeiadis
HIGHLIGHT:      Therefore, we address, in this paper, the reachability problem for programs running under PS 2.0 with relaxed accesses together with promises.

45, TITLE:      Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain
http://arxiv.org/abs/1908.10258
AUTHORS:        Naman Goel ; Cyril van Schreven ; Aris Filos-Ratsikas ; Boi Faltings
HIGHLIGHT:      In this paper, we identify two biggest challenges in building decentralized, trustless and transparent oracles.

46, TITLE:      An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
http://arxiv.org/abs/1908.04812
AUTHORS:        Taesun Whang ; Dongyub Lee ; Chanhee Lee ; Kisu Yang ; Dongsuk Oh ; HeuiSeok Lim
COMMENTS:       INTERSPEECH 2020
HIGHLIGHT:      In this paper, we utilize the powerful pre-trained language model Bi-directional Encoder Representations from Transformer (BERT) for a multi-turn dialog system and propose a highly effective post-training method on domain-specific corpus.

47, TITLE:      EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks
http://arxiv.org/abs/2006.04270
AUTHORS:        Hojjat Salehinejad ; Shahrokh Valaee
HIGHLIGHT:      Inspired by the dropout concept, we propose EDropout as an energy-based framework for pruning neural networks in classification tasks.

48, TITLE:      Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
http://arxiv.org/abs/2004.04668
AUTHORS:        Neerav Karani ; Ertunc Erdil ; Krishna Chaitanya ; Ender Konukoglu
COMMENTS:       Pre-print (currently under review at a Journal)
HIGHLIGHT:      We employ an independently trained denoising autoencoder (DAE) in order to model such an implicit prior on plausible anatomical segmentation labels.

49, TITLE:      Structured Binary Neural Networks for Image Recognition
http://arxiv.org/abs/1909.09934
AUTHORS:        Bohan Zhuang ; Chunhua Shen ; Mingkui Tan ; Peng Chen ; Lingqiao Liu ; Ian Reid
COMMENTS:       16 pages. Extended version of the conference version arXiv:1811.10413
HIGHLIGHT:      We propose methods to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models that are specifically friendly to mobile devices with limited power capacity and computation resources.

50, TITLE:      Domain-Specific Multi-Level IR Rewriting for GPU
http://arxiv.org/abs/2005.13014
AUTHORS:        Tobias Gysi ; Christoph Müller ; Oleksandr Zinenko ; Stephan Herhut ; Eddie Davis ; Tobias Wicky ; Oliver Fuhrer ; Torsten Hoefler ; Tobias Grosser
COMMENTS:       12 pages, 16 figures
HIGHLIGHT:      We demonstrate the effectiveness of this approach for the weather and climate domain.

51, TITLE:      Bayesian Subspace HMM for the Zerospeech 2020 Challenge
http://arxiv.org/abs/2005.09282
AUTHORS:        Bolaji Yusuf ; Lucas Ondel
HIGHLIGHT:      In this paper we describe our submission to the Zerospeech 2020 challenge, where the participants are required to discover latent representations from unannotated speech, and to use those representations to perform speech synthesis, with synthesis quality used as a proxy metric for the unit quality.

52, TITLE:      On the Complexity of Quadratization for Polynomial Differential Equations
http://arxiv.org/abs/2007.08910
AUTHORS:        Mathieu Hemery ; François Fages ; Sylvain Soliman
HIGHLIGHT:      In this paper, we study the theoretical and practical complexities of the quadratic transformation.

53, TITLE:      MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations
http://arxiv.org/abs/2006.02659
AUTHORS:        Qing Yang ; Xia Zhu ; Jong-Kae Fwu ; Yun Ye ; Ganmei You ; Yuan Zhu
HIGHLIGHT:      In this paper, we propose a novel Morphological Fragmental Perturbation Pyramid (MFPP) method to solve the Explainable AI problem.

54, TITLE:      TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
http://arxiv.org/abs/2002.04461
AUTHORS:        Alexander Tong ; Jessie Huang ; Guy Wolf ; David van Dijk ; Smita Krishnaswamy
COMMENTS:       Presented at ICML 2020
HIGHLIGHT:      We present TrajectoryNet, which controls the continuous paths taken between distributions to produce dynamic optimal transport.

55, TITLE:      Vocal markers from sustained phonation in Huntington's Disease
http://arxiv.org/abs/2006.05365
AUTHORS:        Rachid Riad ; Hadrien Titeux ; Laurie Lemoine ; Justine Montillot ; Jennifer Hamet Bagnou ; Xuan Nga Cao ; Emmanuel Dupoux ; Anne-Catherine Bachoud-Lévi
COMMENTS:       submitted to INTERSPEECH 2020. 1 pages of supplementary material appear only in the arxiv version
HIGHLIGHT:      We investigated phonatory impairments as potential clinical markers and propose them for both diagnosis and gene carriers follow-up.

56, TITLE:      UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models
http://arxiv.org/abs/2007.06676
AUTHORS:        Varun Ravi Kumar ; Senthil Yogamani ; Markus Bach ; Christian Witt ; Stefan Milz ; Patrick Mader
COMMENTS:       Camera ready version for publication at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
HIGHLIGHT:      In this paper, we propose a generic scale-aware self-supervised pipeline for estimating depth, euclidean distance, and visual odometry from unrectified monocular videos.

57, TITLE:      PP-YOLO: An Effective and Efficient Implementation of Object Detector
http://arxiv.org/abs/2007.12099
AUTHORS:        Xiang Long ; Kaipeng Deng ; Guanzhong Wang ; Yang Zhang ; Qingqing Dang ; Yuan Gao ; Hui Shen ; Jianguo Ren ; Shumin Han ; Errui Ding ; Shilei Wen
HIGHLIGHT:      The goal of this paper is to implement an object detector with relatively balanced effectiveness and efficiency that can be directly applied in actual application scenarios, rather than propose a novel detection model.

58, TITLE:      Neural Architecture Search for Speech Recognition
http://arxiv.org/abs/2007.08818
AUTHORS:        Shoukang Hu ; Xurong Xie ; Shansong Liu ; Mengzhe Geng ; Xunying Liu ; Helen Meng
COMMENTS:       One of the authors disagrees to put the paper on the arxiv since the paper is not published. So now I would like to apply a formal withdraw of the paper. Hope you can understand our concerns
HIGHLIGHT:      In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two hyper-parameters that heavily affect the performance and model complexity of state-of-the-art factored time delay neural network (TDNN-F) acoustic models: i) the left and right splicing context offsets; and ii) the dimensionality of the bottleneck linear projection at each hidden layer.

59, TITLE:      Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatility
http://arxiv.org/abs/1911.06704
AUTHORS:        Rohit Kaushik ; Shikhar Jain ; Siddhant Jain ; Tirtharaj Dash
COMMENTS:       Preprint (18 pages)
HIGHLIGHT:      Our interest in this paper is to study the applicability of the popular deep neural networks (DNN) as function approximators for non-stationary TSF.

60, TITLE:      Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
http://arxiv.org/abs/1912.11270
AUTHORS:        Ahmad Sakor ; Kuldeep Singh ; Anery Patel ; Maria-Esther Vidal
COMMENTS:       8 pages
HIGHLIGHT:      In this paper, we present Falcon 2.0, first joint entity, and relation linking tool over Wikidata.

61, TITLE:      TSIT: A Simple and Versatile Framework for Image-to-Image Translation
http://arxiv.org/abs/2007.12072
AUTHORS:        Liming Jiang ; Changxu Zhang ; Mingyang Huang ; Chunxiao Liu ; Jianping Shi ; Chen Change Loy
COMMENTS:       ECCV 2020 (Spotlight). Table 2 is updated. GitHub: https://github.com/EndlessSora/TSIT
HIGHLIGHT:      We introduce a simple and versatile framework for image-to-image translation.

62, TITLE:      Word Representation for Rhythms
http://arxiv.org/abs/2007.10610
AUTHORS:        Tongyu Lu ; Lyucheng Yan ; Gus Xia
COMMENTS:       5 pages, 8 figures v2. embellished figures; rearranged section 2; added details for figure captions v3. corrected a typo of author's name
HIGHLIGHT:      This paper proposes a word representation strategy for rhythm patterns.

63, TITLE:      Classifying Constructive Comments
http://arxiv.org/abs/2004.05476
AUTHORS:        Varada Kolhatkar ; Nithum Thain ; Jeffrey Sorensen ; Lucas Dixon ; Maite Taboada
HIGHLIGHT:      We introduce the Constructive Comments Corpus (C3), comprised of 12,000 annotated news comments, intended to help build new tools for online communities to improve the quality of their discussions.

64, TITLE:      COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
http://arxiv.org/abs/2007.07431
AUTHORS:        Kuniaki Saito ; Kate Saenko ; Ming-Yu Liu
COMMENTS:       The paper will be presented at the EUROPEAN Conference on Computer Vision (ECCV) 2020
HIGHLIGHT:      To address the issue, we propose a new few-shot image translation model, COCO-FUNIT, which computes the style embedding of the example images conditioned on the input image and a new module called the constant style bias.

65, TITLE:      Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response
http://arxiv.org/abs/1907.04394
AUTHORS:        Payam Ghassemi ; David DePauw ; Souma Chowdhury
COMMENTS:       Accepted for poster presentation in (and publication as extended abstract in the proceedings of) The IEEE 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)
HIGHLIGHT:      To provision these capabilities, this paper presents a computationally efficient algorithm that involves novel construction and matching of bipartite graphs.

66, TITLE:      Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations
http://arxiv.org/abs/2007.08749
AUTHORS:        Benjamin Schloss ; Sandeep Konam
COMMENTS:       21 pages,1 figure
HIGHLIGHT:      In this paper, we bridge this gap for two tasks: classifying utterances from medical conversations according to (i) the SOAP section and (ii) the speaker role.

67, TITLE:      On the Variational Posterior of Dirichlet Process Deep Latent Gaussian Mixture Models
http://arxiv.org/abs/2006.08993
AUTHORS:        Amine Echraibi ; Joachim Flocon-Cholet ; Stéphane Gosselin ; Sandrine Vaton
HIGHLIGHT:      In this paper, we present an alternative treatment of the variational posterior of the Dirichlet Process Deep Latent Gaussian Mixture Model (DP-DLGMM), where we show that the prior cluster parameters and the variational posteriors of the beta distributions and cluster hidden variables can be updated in closed-form.

68, TITLE:      Distillation Guided Residual Learning for Binary Convolutional Neural Networks
http://arxiv.org/abs/2007.05223
AUTHORS:        Jianming Ye ; Shiliang Zhang ; Jingdong Wang
HIGHLIGHT:      We observe that, this performance gap leads to substantial residuals between intermediate feature maps of BCNN and FCNN.

69, TITLE:      Detecting Human-Object Interactions with Action Co-occurrence Priors
http://arxiv.org/abs/2007.08728
AUTHORS:        Dong-Jin Kim ; Xiao Sun ; Jinsoo Choi ; Stephen Lin ; In So Kweon
COMMENTS:       ECCV 2020. Source code : https://github.com/Dong-JinKim/ActionCooccurrencePriors/
HIGHLIGHT:      In this paper, we model the correlations as action co-occurrence matrices and present techniques to learn these priors and leverage them for more effective training, especially in rare classes.

70, TITLE:      Conditional Convolutions for Instance Segmentation
http://arxiv.org/abs/2003.05664
AUTHORS:        Zhi Tian ; Chunhua Shen ; Hao Chen
COMMENTS:       Accepted to Proc. European Conf. Computer Vision (ECCV) 2020 as oral presentation
HIGHLIGHT:      We propose a simple yet effective instance segmentation framework, termed CondInst (conditional convolutions for instance segmentation).

71, TITLE:      ITSELF: Iterative Saliency Estimation fLexible Framework
http://arxiv.org/abs/2006.16956
AUTHORS:        Leonardo de Melo Joao ; Felipe de Castro Belem ; Alexandre Xavier Falcao
HIGHLIGHT:      By combining a saliency-based superpixel algorithm to a superpixel-based saliency estimator, we propose a novel saliency/superpixel self-improving loop to iteratively enhance saliency maps.

72, TITLE:      No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph
http://arxiv.org/abs/1809.10044
AUTHORS:        Kuldeep Singh ; Ioanna Lytra ; Arun Sethupat Radhakrishna ; Saeedeh Shekarpour ; Maria-Esther Vidal ; Jens Lehmann
COMMENTS:       Evaluation of State of the art Question Answering components performing entity linking, relation linking etc
HIGHLIGHT:      In this article, we analyse and micro evaluate the behaviour of 29 available QA components for DBpedia knowledge graph that were released by the research community since 2010. As a result, we provide a perspective on collective failure cases, suggest characteristics of QA components that prevent them from performing better and provide future challenges and research directions for the field.

73, TITLE:      The phonetic bases of vocal expressed emotion: natural versus acted
http://arxiv.org/abs/1911.05733
AUTHORS:        Hira Dhamyal ; Shahan Ali Memon ; Bhiksha Raj ; Rita Singh
COMMENTS:       5 pages, 6 figures
HIGHLIGHT:      In this paper, we present an on-scale systematic study of the differences between natural and acted vocal emotions.

74, TITLE:      Object-Contextual Representations for Semantic Segmentation
http://arxiv.org/abs/1909.11065
AUTHORS:        Yuhui Yuan ; Xilin Chen ; Jingdong Wang
COMMENTS:       ECCV 2020 Spotlight. Project Page: https://github.com/openseg-group/openseg.pytorch https://github.com/HRNet/HRNet-Semantic-Segmentation/tree/HRNet-OCR
HIGHLIGHT:      In this paper, we address the semantic segmentation problem with a focus on the context aggregation strategy.

75, TITLE:      Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer
http://arxiv.org/abs/2003.05117
AUTHORS:        Krishan Rana ; Vibhavari Dasagi ; Ben Talbot ; Michael Milford ; Niko Sünderhauf
COMMENTS:       Accepted for presentation at IROS2020. Project site available at https://sites.google.com/view/mcf-nav/home
HIGHLIGHT:      We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment.

76, TITLE:      A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization
http://arxiv.org/abs/1805.03616
AUTHORS:        Li Wang ; Junlin Yao ; Yunzhe Tao ; Li Zhong ; Wei Liu ; Qiang Du
COMMENTS:       International Joint Conference on Artificial Intelligence and European Conference on Artificial Intelligence (IJCAI-ECAI), 2018
HIGHLIGHT:      In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization.

77, TITLE:      Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition
http://arxiv.org/abs/2007.10298
AUTHORS:        Darshan Gera ; S Balasubramanian
COMMENTS:       A couple of reference citations corrected, few details added and code link provided
HIGHLIGHT:      Leveraging on the aforementioned observations, an end-to-end architecture for FER is proposed in this work that obtains both local and global attention per channel per spatial location through a novel spatio-channel attention net (SCAN), without seeking any information from the landmark detectors.

78, TITLE:      Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
http://arxiv.org/abs/2003.02484
AUTHORS:        Saehyung Lee ; Hyungyu Lee ; Sungroh Yoon
COMMENTS:       To appear in CVPR 2020 (Oral)
HIGHLIGHT:      In this paper, we identify Adversarial Feature Overfitting (AFO), which may cause poor adversarially robust generalization, and we show that adversarial training can overshoot the optimal point in terms of robust generalization, leading to AFO in our simple Gaussian model.

79, TITLE:      Towards Bounding-Box Free Panoptic Segmentation
http://arxiv.org/abs/2002.07705
AUTHORS:        Ujwal Bonde ; Pablo F. Alcantarilla ; Stefan Leutenegger
COMMENTS:       15 pages, 6 figures
HIGHLIGHT:      In this work we introduce a new Bounding-Box Free Network (BBFNet) for panoptic segmentation.

80, TITLE:      Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints
http://arxiv.org/abs/2005.13109
AUTHORS:        Shushman Choudhury ; Jayesh K. Gupta ; Mykel J. Kochenderfer ; Dorsa Sadigh ; Jeannette Bohg
COMMENTS:       Robotics Science and Systems (RSS) 2020; Source code at https://github.com/sisl/SCoBA.jl
HIGHLIGHT:      We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination and addresses them in a hierarchical manner.

81, TITLE:      Contrastive Learning for Weakly Supervised Phrase Grounding
http://arxiv.org/abs/2006.09920
AUTHORS:        Tanmay Gupta ; Arash Vahdat ; Gal Chechik ; Xiaodong Yang ; Jan Kautz ; Derek Hoiem
COMMENTS:       Project page: http://tanmaygupta.info/info-ground
HIGHLIGHT:      We show that phrase grounding can be learned by optimizing word-region attention to maximize a lower bound on mutual information between images and caption words.

82, TITLE:      Top-Related Meta-Learning Method for Few-Shot Detection
http://arxiv.org/abs/2007.06837
AUTHORS:        Qian Li ; Nan Guo ; Duo Wang ; Xiaochun Ye ; Dongrui Fan ; Zhimin Tang
COMMENTS:       meta-learing,few-shot detection, category-based grouping mechanism
HIGHLIGHT:      In this paper, we find that the main challenge lies on imbalance between the examples, and poor shared distribution of class-based meta-features.

83, TITLE:      A scalable constructive algorithm for the optimization of neural network architectures
http://arxiv.org/abs/1909.03306
AUTHORS:        Massimiliano Lupo Pasini ; Junqi Yin ; Ying Wai Li ; Markus Eisenbach
COMMENTS:       23 pages, 4 figures, 1 table
HIGHLIGHT:      In this work we propose a new method to optimize the architecture of an artificial neural network.

84, TITLE:      Adaptive spline fitting with particle swarm optimization
http://arxiv.org/abs/1907.12160
AUTHORS:        Soumya D. Mohanty ; Ethan Fahnestock
COMMENTS:       Accepted version; Typo corrected in equation 3; Minor changes to text
HIGHLIGHT:      We present a method that uses particle swarm optimization (PSO) combined with model selection to address this challenge.

85, TITLE:      Fast Complete Algorithm for Multiplayer Nash Equilibrium
http://arxiv.org/abs/2002.04734
AUTHORS:        Sam Ganzfried
HIGHLIGHT:      We describe a new complete algorithm for computing Nash equilibrium in multiplayer general-sum games, based on a quadratically-constrained feasibility program formulation.

86, TITLE:      Machine Learning Explanations to Prevent Overtrust in Fake News Detection
http://arxiv.org/abs/2007.12358
AUTHORS:        Sina Mohseni ; Fan Yang ; Shiva Pentyala ; Mengnan Du ; Yi Liu ; Nic Lupfer ; Xia Hu ; Shuiwang Ji ; Eric Ragan
HIGHLIGHT:      We present evaluation results and analysis from multiple controlled crowdsourced studies. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms to study the effects of algorithmic transparency on end-users.

87, TITLE:      Network Pruning via Annealing and Direct Sparsity Control
http://arxiv.org/abs/2002.04301
AUTHORS:        Yangzi Guo ; Yiyuan She ; Adrian Barbu
HIGHLIGHT:      In this paper, we propose a novel efficient network pruning method that is suitable for both non-structured and structured channel-level pruning.

88, TITLE:      Input-output equations and identifiability of linear ODE models
http://arxiv.org/abs/1910.03960
AUTHORS:        Alexey Ovchinnikov ; Gleb Pogudin ; Peter Thompson
COMMENTS:       For better focus and presentation purposes, most of the appendix has been removed to become a part of a stand-alone article
HIGHLIGHT:      In this paper, to address this, we prove identifiability of the coefficients of input-output equations for types of differential models that often appear in practice, such as linear models with one output and linear compartment models in which, from each compartment, one can reach either a leak or an input.

89, TITLE:      Data Techniques For Online End-to-end Speech Recognition
http://arxiv.org/abs/2001.09221
AUTHORS:        Yang Chen ; Weiran Wang ; I-Fan Chen ; Chao Wang
COMMENTS:       5 pages, 1 figure
HIGHLIGHT:      In this work, we explore a few simple-to-implement techniques for building online ASR systems in an end-to-end fashion, with a small amount of transcribed data in the target domain.

90, TITLE:      COVI White Paper
http://arxiv.org/abs/2005.08502
AUTHORS:        Hannah Alsdurf ; Edmond Belliveau ; Yoshua Bengio ; Tristan Deleu ; Prateek Gupta ; Daphne Ippolito ; Richard Janda ; Max Jarvie ; Tyler Kolody ; Sekoul Krastev ; Tegan Maharaj ; Robert Obryk ; Dan Pilat ; Valerie Pisano ; Benjamin Prud'homme ; Meng Qu ; Nasim Rahaman ; Irina Rish ; Jean-Francois Rousseau ; Abhinav Sharma ; Brooke Struck ; Jian Tang ; Martin Weiss ; Yun William Yu
COMMENTS:       64 pages, 1 figure
HIGHLIGHT:      Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.

91, TITLE:      Specifying and Computing Causes for Query Answers in Databases via Database Repairs and Repair Programs
http://arxiv.org/abs/1712.01001
AUTHORS:        Leopoldo Bertossi
COMMENTS:       Revised and slightly extended version of journal submission after reviews. This is a much revised, corrected and extended version of: Bertossi, L. "Characterizing and Computing Causes for Query Answers in Databases from Database Repairs and Repair Programs". Proc. FoIKs, 2018, Springer LNCS 10833, pp. 55-76
HIGHLIGHT:      In this work, answer-set programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes.

92, TITLE:      Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators
http://arxiv.org/abs/1905.10710
AUTHORS:        Alexander Tong ; Guy Wolf ; Smita Krishnaswamy
COMMENTS:       6 pages, 4 figures, 2 tables, presented at IEEE MLSP
HIGHLIGHT:      Instead, we introduce a new unsupervised Lipschitz anomaly discriminator that does not suffer from these biases.

93, TITLE:      Efficient resource management in UAVs for Visual Assistance
http://arxiv.org/abs/2007.05854
AUTHORS:        Bapireddy Karri ; Sudip Misra
COMMENTS:       Problem with the results in submitted version
HIGHLIGHT:      This projects describes a novel method to optimize the general image processing tasks like object tracking and object detection for UAV hardware in real time scenarios without affecting the flight time and not tampering the latency and accuracy of these models.

94, TITLE:      Almost Unsupervised Text to Speech and Automatic Speech Recognition
http://arxiv.org/abs/1905.06791
AUTHORS:        Yi Ren ; Xu Tan ; Tao Qin ; Sheng Zhao ; Zhou Zhao ; Tie-Yan Liu
COMMENTS:       Accepted by ICML2019
HIGHLIGHT:      In this paper, by leveraging the dual nature of the two tasks, we propose an almost unsupervised learning method that only leverages few hundreds of paired data and extra unpaired data for TTS and ASR.

95, TITLE:      Learning Differentiable Programs with Admissible Neural Heuristics
http://arxiv.org/abs/2007.12101
AUTHORS:        Ameesh Shah ; Eric Zhan ; Jennifer J. Sun ; Abhinav Verma ; Yisong Yue ; Swarat Chaudhuri
COMMENTS:       9 pages, under review
HIGHLIGHT:      We study the problem of learning differentiable functions expressed as programs in a domain-specific language.

96, TITLE:      Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs
http://arxiv.org/abs/2006.10147
AUTHORS:        Nicolae-Cătălin Ristea ; Radu Tudor Ionescu
COMMENTS:       Accepted at INTERSPEECH 2020
HIGHLIGHT:      In this paper, we propose a novel data augmentation approach for mask detection from speech.

97, TITLE:      ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions
http://arxiv.org/abs/2007.12212
AUTHORS:        Anurag Roy ; Vinay Kumar Verma ; Kripabandhu Ghosh ; Saptarshi Ghosh
COMMENTS:       Accepted in CIKM-2020
HIGHLIGHT:      In this paper, we propose a novel GAN-based model for zero-shot text to image retrieval.

98, TITLE:      Improving Object Detection with Selective Self-supervised Self-training
http://arxiv.org/abs/2007.09162
AUTHORS:        Yandong Li ; Di Huang ; Danfeng Qin ; Liqiang Wang ; Boqing Gong
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      To tackle this challenge, we propose a selective net to rectify the supervision signals in Web images.

99, TITLE:      Region-based Non-local Operation for Video Classification
http://arxiv.org/abs/2007.09033
AUTHORS:        Guoxi Huang ; Adrian G. Bors
HIGHLIGHT:      This paper presents region-based non-local operation (RNL), a family of self-attention mechanisms, which can directly capture long-range dependencies without a deep stack of local operations.

100, TITLE:     Unsupervisedly Learned Representations: Should the Quest be Over?
http://arxiv.org/abs/2001.07495
AUTHORS:        Daniel N. Nissani
COMMENTS:       To be published at The 6th International Conference on Machine Learning, Optimization and Data Science - LOD 2020
HIGHLIGHT:      We present in this paper a possible solution to this puzzle.

101, TITLE:     Multi-branch Attentive Transformer
http://arxiv.org/abs/2006.10270
AUTHORS:        Yang Fan ; Shufang Xie ; Yingce Xia ; Lijun Wu ; Tao Qin ; Xiang-Yang Li ; Tie-Yan Liu
COMMENTS:       17 pages
HIGHLIGHT:      In this work, we propose a simple yet effective variant of Transformer called multi-branch attentive Transformer (briefly, MAT), where the attention layer is the average of multiple branches and each branch is an independent multi-head attention layer.

102, TITLE:     Autoencoding with XCSF
http://arxiv.org/abs/1910.10579
AUTHORS:        Richard J. Preen ; Stewart W. Wilson ; Larry Bull
HIGHLIGHT:      This article explores the use of the online evolutionary machine learning system XCSF to perform autoencoding.

103, TITLE:     MVLoc: Multimodal Variational Geometry-Aware Learning for Visual Localization
http://arxiv.org/abs/2003.07289
AUTHORS:        Rui Zhou ; Changhao Chen ; Bing Wang ; Andrew Markham ; Niki Trigoni
COMMENTS:       There exist several serious typos in this paper. And some results are problematic due to the wrong experiment
HIGHLIGHT:      To address this, we propose an end-to-end framework to fuse different sensor inputs through a variational Product-of-Experts (PoE) joint encoder followed by attention-based fusion.

104, TITLE:     A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors
http://arxiv.org/abs/1909.07267
AUTHORS:        Jiawei Mo ; Junaed Sattar
COMMENTS:       Accepted by IROS2020
HIGHLIGHT:      In this paper, we propose an alternative approach adapting LiDAR descriptors for 3D points obtained from stereo-visual odometry for place recognition.

105, TITLE:     SummEval: Re-evaluating Summarization Evaluation
http://arxiv.org/abs/2007.12626
AUTHORS:        Alexander R. Fabbri ; Wojciech Kryściński ; Bryan McCann ; Caiming Xiong ; Richard Socher ; Dragomir Radev
COMMENTS:       10 pages, 4 tables, 1 figure
HIGHLIGHT:      We address the existing shortcomings of summarization evaluation methods along five dimensions: 1) we re-evaluate 12 automatic evaluation metrics in a comprehensive and consistent fashion using neural summarization model outputs along with expert and crowd-sourced human annotations, 2) we consistently benchmark 23 recent summarization models using the aforementioned automatic evaluation metrics, 3) we assemble the largest collection of summaries generated by models trained on the CNN/DailyMail news dataset and share it in a unified format, 4) we implement and share a toolkit that provides an extensible and unified API for evaluating summarization models across a broad range of automatic metrics, 5) we assemble and share the largest and most diverse, in terms of model types, collection of human judgments of model-generated summaries on the CNN/Daily Mail dataset annotated by both expert judges and crowd source workers.

106, TITLE:     Analysis of Bayesian Networks via Prob-Solvable Loops
http://arxiv.org/abs/2007.09450
AUTHORS:        Ezio Bartocci ; Laura Kovács ; Miroslav Stankovič
HIGHLIGHT:      In this paper we extend Prob-solvable loops with new features essential for encoding Bayesian networks (BNs).

107, TITLE:     Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario
http://arxiv.org/abs/2005.07272
AUTHORS:        Ivan Medennikov ; Maxim Korenevsky ; Tatiana Prisyach ; Yuri Khokhlov ; Mariya Korenevskaya ; Ivan Sorokin ; Tatiana Timofeeva ; Anton Mitrofanov ; Andrei Andrusenko ; Ivan Podluzhny ; Aleksandr Laptev ; Aleksei Romanenko
COMMENTS:       Accepted to Interspeech 2020
HIGHLIGHT:      We propose a novel Target-Speaker Voice Activity Detection (TS-VAD) approach, which directly predicts an activity of each speaker on each time frame.

108, TITLE:     Bitcoin covenants unchained
http://arxiv.org/abs/2006.03918
AUTHORS:        Massimo Bartoletti ; Stefano Lande ; Roberto Zunino
HIGHLIGHT:      In this paper we propose a formal model of covenants, which can be implemented with minor modifications to Bitcoin.

109, TITLE:     Term Revealing: Furthering Quantization at Run Time on Quantized DNNs
http://arxiv.org/abs/2007.06389
AUTHORS:        H. T. Kung ; Bradley McDanel ; Sai Qian Zhang
COMMENTS:       13 pages, 19 figures, 4 tables, To appear in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2020 Update: Revised writing/figures and added more references for Section IV Update: Revised Section IV writing/figures and added additional references on signed digit representations
HIGHLIGHT:      We present a novel technique, called Term Revealing (TR), for furthering quantization at run time for improved performance of Deep Neural Networks (DNNs) already quantized with conventional quantization methods.

110, TITLE:     Graph Optimal Transport for Cross-Domain Alignment
http://arxiv.org/abs/2006.14744
AUTHORS:        Liqun Chen ; Zhe Gan ; Yu Cheng ; Linjie Li ; Lawrence Carin ; Jingjing Liu
HIGHLIGHT:      We propose Graph Optimal Transport (GOT), a principled framework that germinates from recent advances in Optimal Transport (OT).

111, TITLE:     Learning Pose-invariant 3D Object Reconstruction from Single-view Images
http://arxiv.org/abs/2004.01347
AUTHORS:        Bo Peng ; Wei Wang ; Jing Dong ; Tieniu Tan
COMMENTS:       under review, code available at https://github.com/bomb2peng/learn3D
HIGHLIGHT:      In this paper, we relax the common multi-view assumption and explore a more challenging yet more realistic setup of learning 3D shape from only single-view images.

112, TITLE:     Suppress and Balance: A Simple Gated Network for Salient Object Detection
http://arxiv.org/abs/2007.08074
AUTHORS:        Xiaoqi Zhao ; Youwei Pang ; Lihe Zhang ; Huchuan Lu ; Lei Zhang
COMMENTS:       Accepted in ECCV2020(oral). Code: https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency
HIGHLIGHT:      In this work, we propose a simple gated network (GateNet) to solve both issues at once.

113, TITLE:     Dependent Pearl: Normalization by realizability
http://arxiv.org/abs/1908.09123
AUTHORS:        Pierre-Évariste Dagand ; Lionel Rieg ; Gabriel Scherer
HIGHLIGHT:      The semantic technique we set out to study is Krivine's classical realizability, which amounts to a proof-relevant presentation of reducibility arguments -- unary logical relations.

114, TITLE:     Privacy Preserving Visual SLAM
http://arxiv.org/abs/2007.10361
AUTHORS:        Mikiya Shibuya ; Shinya Sumikura ; Ken Sakurada
COMMENTS:       ECCV2020, Project: https://xdspacelab.github.io/lcvslam/ , Video: https://youtu.be/gEtUqnHx83w
HIGHLIGHT:      This study proposes a privacy-preserving Visual SLAM framework for estimating camera poses and performing bundle adjustment with mixed line and point clouds in real time.

115, TITLE:     Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
http://arxiv.org/abs/2004.06165
AUTHORS:        Xiujun Li ; Xi Yin ; Chunyuan Li ; Pengchuan Zhang ; Xiaowei Hu ; Lei Zhang ; Lijuan Wang ; Houdong Hu ; Li Dong ; Furu Wei ; Yejin Choi ; Jianfeng Gao
COMMENTS:       ECCV 2020, Code and pre-trained models are released: https://github.com/microsoft/Oscar
HIGHLIGHT:      Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

116, TITLE:     Pillar-based Object Detection for Autonomous Driving
http://arxiv.org/abs/2007.10323
AUTHORS:        Yue Wang ; Alireza Fathi ; Abhijit Kundu ; David Ross ; Caroline Pantofaru ; Thomas Funkhouser ; Justin Solomon
COMMENTS:       Accepted to ECCV2020
HIGHLIGHT:      We present a simple and flexible object detection framework optimized for autonomous driving.

117, TITLE:     Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network
http://arxiv.org/abs/2003.02944
AUTHORS:        Haowen Fang ; Amar Shrestha ; Ziyi Zhao ; Qinru Qiu
HIGHLIGHT:      We proposed a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights.

118, TITLE:     Learning to Match Distributions for Domain Adaptation
http://arxiv.org/abs/2007.10791
AUTHORS:        Chaohui Yu ; Jindong Wang ; Chang Liu ; Tao Qin ; Renjun Xu ; Wenjie Feng ; Yiqiang Chen ; Tie-Yan Liu
COMMENTS:       Preprint. 20 Pages. Code available at https://github.com/jindongwang/transferlearning/tree/master/code/deep/Learning-to-Match
HIGHLIGHT:      This paper proposes Learning to Match (L2M) to automatically learn the cross-domain distribution matching without relying on hand-crafted priors on the matching loss.

119, TITLE:     Dynamic Sparsity Neural Networks for Automatic Speech Recognition
http://arxiv.org/abs/2005.10627
AUTHORS:        Zhaofeng Wu ; Ding Zhao ; Qiao Liang ; Jiahui Yu ; Anmol Gulati ; Ruoming Pang
HIGHLIGHT:      In this paper, generalizing from slimmable neural networks, we present dynamic sparsity neural networks (DSNN) that, once trained, can instantly switch to execute at any given sparsity level at run-time.

120, TITLE:     Unsupervised Domain Adaptation on Reading Comprehension
http://arxiv.org/abs/1911.06137
AUTHORS:        Yu Cao ; Meng Fang ; Baosheng Yu ; Joey Tianyi Zhou
COMMENTS:       8 pages, 6 figures, 5 tables, Accepted by AAAI 2020
HIGHLIGHT:      To solve this, we provide a novel conditional adversarial self-training method (CASe).

121, TITLE:     Information Extraction of Clinical Trial Eligibility Criteria
http://arxiv.org/abs/2006.07296
AUTHORS:        Yitong Tseo ; M. I. Salkola ; Ahmed Mohamed ; Anuj Kumar ; Freddy Abnousi
COMMENTS:       4 pages
HIGHLIGHT:      In this paper, we investigate an information extraction (IE) approach for grounding criteria from trials in ClinicalTrials(dot)gov to a shared knowledge base.

122, TITLE:     Deformable 3D Convolution for Video Super-Resolution
http://arxiv.org/abs/2004.02803
AUTHORS:        Xinyi Ying ; Longguang Wang ; Yingqian Wang ; Weidong Sheng ; Wei An ; Yulan Guo
HIGHLIGHT:      In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR.

123, TITLE:     Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset
http://arxiv.org/abs/2006.08274
AUTHORS:        Andrei Andrusenko ; Aleksandr Laptev ; Ivan Medennikov
COMMENTS:       Accepted by SPECOM 2020
HIGHLIGHT:      This paper presents an exploration of end-to-end automatic speech recognition systems (ASR) for the largest open-source Russian language data set -- OpenSTT.