2020.07.22.txt

==========New Papers==========
1, TITLE:       Shape and Viewpoint without Keypoints
http://arxiv.org/abs/2007.10982
AUTHORS:        Shubham Goel ; Angjoo Kanazawa ; Jitendra Malik
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      We present a learning framework that learns to recover the 3D shape, pose and texture from a single image, trained on an image collection without any ground truth 3D shape, multi-view, camera viewpoints or keypoint supervision.

2, TITLE:       Balanced Meta-Softmax for Long-Tailed Visual Recognition
http://arxiv.org/abs/2007.10740
AUTHORS:        Jiawei Ren ; Cunjun Yu ; Shunan Sheng ; Xiao Ma ; Haiyu Zhao ; Shuai Yi ; Hongsheng Li
HIGHLIGHT:      In this paper, we show that Softmax function, though used in most classification tasks, gives a biased gradient estimation under the long-tailed setup.

3, TITLE:       Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling
http://arxiv.org/abs/2007.10983
AUTHORS:        Yuliang Zou ; Pan Ji ; Quoc-Huy Tran ; Jia-Bin Huang ; Manmohan Chandraker
COMMENTS:       ECCV 2020. Project page: https://yuliang.vision/LTMVO
HIGHLIGHT:      In this paper, we present a self-supervised learning method for VO with special consideration for consistency over longer sequences.

4, TITLE:       Foley Music: Learning to Generate Music from Videos
http://arxiv.org/abs/2007.10984
AUTHORS:        Chuang Gan ; Deng Huang ; Peihao Chen ; Joshua B. Tenenbaum ; Antonio Torralba
COMMENTS:       ECCV 2020. Project page: http://foley-music.csail.mit.edu
HIGHLIGHT:      In this paper, we introduce Foley Music, a system that can synthesize plausible music for a silent video clip about people playing musical instruments.

5, TITLE:       Effects of Approximate Multiplication on Convolutional Neural Networks
http://arxiv.org/abs/2007.10500
AUTHORS:        Min Soo Kim ; Alberto A. Del Barrio ; HyunJin Kim ; Nader Bagherzadeh
COMMENTS:       12 pages, 11 figures, 4 tables, submitted to IEEE TETC
HIGHLIGHT:      The most far-reaching contribution of this paper is the analytical justification that multiplications can be approximated while additions need to be exact in CNN MAC operations.

6, TITLE:       PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
http://arxiv.org/abs/2007.10985
AUTHORS:        Saining Xie ; Jiatao Gu ; Demi Guo ; Charles R. Qi ; Leonidas J. Guibas ; Or Litany
COMMENTS:       ECCV 2020 (Spotlight)
HIGHLIGHT:      In this work, we aim at facilitating research on 3D representation learning.

7, TITLE:       The Two-Armed Balance Game: Probability, Random Strategy and Dishonesty
http://arxiv.org/abs/2007.10735
AUTHORS:        Fangqi Li
COMMENTS:       11 pages, 4 figures,
HIGHLIGHT:      In this paper some variants of the balance game are discussed, especially from a probabilistic perspective.

8, TITLE:       Movement Assessment from Skeleton Videos: A Review
http://arxiv.org/abs/2007.10737
AUTHORS:        Tal Hakim
HIGHLIGHT:      In this paper, we divide the movement assessment task into secondary tasks and explain why they are needed and how they can be addressed.

9, TITLE:       Video Representation Learning by Recognizing Temporal Transformations
http://arxiv.org/abs/2007.10730
AUTHORS:        Simon Jenni ; Givi Meishvili ; Paolo Favaro
COMMENTS:       ECCV 2020
HIGHLIGHT:      We introduce a novel self-supervised learning approach to learn representations of videos that are responsive to changes in the motion dynamics.

10, TITLE:      Neural Mesh Flow: 3D Manifold Mesh Generationvia Diffeomorphic Flows
http://arxiv.org/abs/2007.10973
AUTHORS:        Kunal Gupta ; Manmohan Chandraker
COMMENTS:       Project Page: https://kunalmgupta.github.io/projects/NeuralMeshflow.html
HIGHLIGHT:      In this work, we propose Neural Mesh Flow (NMF) to generate two-manifold meshes for genus-0 shapes.

11, TITLE:      Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
http://arxiv.org/abs/2007.10732
AUTHORS:        Shuailin Li ; Chuyu Zhang ; Xuming He
COMMENTS:       Appear in MICCAI2020
HIGHLIGHT:      In this work, we propose a novel shapeaware semi-supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output.

12, TITLE:      Learning to Compose Hypercolumns for Visual Correspondence
http://arxiv.org/abs/2007.10587
AUTHORS:        Juhong Min ; Jongmin Lee ; Jean Ponce ; Minsu Cho
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      In this work, we introduce a novel approach to visual correspondence that dynamically composes effective features by leveraging relevant layers conditioned on the images to match.

13, TITLE:      CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers
http://arxiv.org/abs/2007.10588
AUTHORS:        Jinpyo Kim ; Wooekun Jung ; Hyungmo Kim ; Jaejin Lee
HIGHLIGHT:      This paper proposes a deep CNN model, called CyCNN, which exploits polar mapping of input images to convert rotation to translation.

14, TITLE:      AinnoSeg: Panoramic Segmentation with High Perfomance
http://arxiv.org/abs/2007.10591
AUTHORS:        Jiahong Wu ; Jianfei Lu ; Xinxin Kang ; Yiming Zhang ; Yinhang Tang ; Jianfei Song ; Ze Huang ; Shenglan Ben ; Jiashui Huang ; Faen Zhang
HIGHLIGHT:      Aiming to address these issues, this paper presents some useful tricks.

15, TITLE:      Optimization of data-driven filterbank for automatic speaker verification
http://arxiv.org/abs/2007.10729
AUTHORS:        Susanta Sarangi ; Md Sahidullah ; Goutam Saha
COMMENTS:       Published in Digital Signal Processing journal (Elsevier)
HIGHLIGHT:      In this paper, we propose a new data-driven filter design method which optimizes filter parameters from a given speech data.

16, TITLE:      Domain Generalization with Optimal Transport and Metric Learning
http://arxiv.org/abs/2007.10573
AUTHORS:        Fan Zhou ; Zhuqing Jiang ; Changjian Shui ; Boyu Wang ; Brahim Chaib-draa
HIGHLIGHT:      We tackle the domain generalization problem to learn from multiple source domains and generalize to a target domain with unknown statistics.

17, TITLE:      Procrustean Regression Networks: Learning 3D Structure of Non-Rigid Objects from 2D Annotations
http://arxiv.org/abs/2007.10961
AUTHORS:        Sungheon Park ; Minsik Lee ; Nojun Kwak
COMMENTS:       ECCV 2020
HIGHLIGHT:      We propose a novel framework for training neural networks which is capable of learning 3D information of non-rigid objects when only 2D annotations are available as ground truths.

18, TITLE:      Recurrent Exposure Generation for Low-Light Face Detection
http://arxiv.org/abs/2007.10963
AUTHORS:        Jinxiu Liang ; Jingwen Wang ; Yuhui Quan ; Tianyi Chen ; Jiaying Liu ; Haibin Ling ; Yong Xu
COMMENTS:       11 pages
HIGHLIGHT:      Fortunately, as shown in this paper, neither is such high-quality necessary since our task is face detection rather than image enhancement.

19, TITLE:      On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic
http://arxiv.org/abs/2007.10712
AUTHORS:        Md Rabiul Awal ; Rui Cao ; Sandra Mitrovic ; Roy Ka-Wei Lee
HIGHLIGHT:      In this paper, we fill the research gap by collecting and annotating a large dataset of over 40 million COVID-19 related tweets.

20, TITLE:      IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection
http://arxiv.org/abs/2007.10820
AUTHORS:        Vipul Singhal ; Sahil Dhull ; Rishabh Agarwal ; Ashutosh Modi
COMMENTS:       6 pages, 3 figures, 3 tables. Accepted at Proceedings of 14th International Workshop on Semantic Evaluation (SemEval-2020)
HIGHLIGHT:      This paper describes the system proposed for addressing the research problem posed in Task 10 of SemEval-2020: Emphasis Selection For Written Text in Visual Media.

21, TITLE:      Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images
http://arxiv.org/abs/2007.10714
AUTHORS:        Tianwen Zhang ; Xiaoling Zhang ; Jun Shi ; Shunjun Wei ; Jianguo Wang ; Jianwei Li ; Hao Su ; Yue Zhou
HIGHLIGHT:      This letter proposes a novel Balance Scene Learning Mechanism (BSLM) for both offshore and inshore ship detection in SAR images.

22, TITLE:      Human Abnormality Detection Based on Bengali Text
http://arxiv.org/abs/2007.10718
AUTHORS:        M. F. Mridha ; Md. Saifur Rahman ; Abu Quwsar Ohi
COMMENTS:       The paper is accepted in IEEE Region 10 Symposium (TENSYMP) 2020
HIGHLIGHT:      In this paper, an efficient and effective human abnormality detection model is introduced, that only uses Bengali text. We have created our Bengali dataset (contains 2000 sentences) that is generated by voluntary conversations.

23, TITLE:      Lymphocyte counting -- Error Analysis of Regression versus Bounding Box Detection Approaches
http://arxiv.org/abs/2007.10817
AUTHORS:        Lin Geng Foo ; Alexander Binder
COMMENTS:       Submitted to ICPR 2020
HIGHLIGHT:      We find from a fine-grained analysis of MSE errors that all models suffer from a substantial underestimation bias.

24, TITLE:      BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text
http://arxiv.org/abs/2007.10819
AUTHORS:        Ayush Kumar ; Harsh Agarwal ; Keshav Bansal ; Ashutosh Modi
COMMENTS:       6 pages, 8 figures, 2 tables. Accepted at Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020)
HIGHLIGHT:      In this paper, we present an ensemble architecture of convolutional neural net (CNN) and self-attention based LSTM for sentiment analysis of code-mixed tweets.

25, TITLE:      3D Correspondence Grouping with Compatibility Features
http://arxiv.org/abs/2007.10570
AUTHORS:        Jiaqi Yang ; Jiahao Chen ; Zhiqiang Huang ; Siwen Quan ; Yanning Zhang ; Zhiguo Cao
HIGHLIGHT:      We present a simple yet effective method for 3D correspondence grouping.

26, TITLE:      Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications
http://arxiv.org/abs/2007.10799
AUTHORS:        Teng Liu ; Xing Yang ; Hong Wang ; Xiaolin Tang ; Long Chen ; Huilong Yu ; Fei-Yue Wang
COMMENTS:       6 pages, 8 figures
HIGHLIGHT:      Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications

27, TITLE:      Unified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing
http://arxiv.org/abs/2007.10558
AUTHORS:        Yapeng Tian ; Dingzeyu Li ; Chenliang Xu
COMMENTS:       ECCV 2020 (Spotlight)
HIGHLIGHT:      In this paper, we introduce a new problem, named audio-visual video parsing, which aims to parse a video into temporal event segments and label them as either audible, visible, or both. To facilitate exploration, we collect a Look, Listen, and Parse (LLP) dataset to investigate audio-visual video parsing in a weakly-supervised manner.

28, TITLE:      Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop
http://arxiv.org/abs/2007.10546
AUTHORS:        Shagun Sodhani ; Mayoore S. Jaiswal ; Lauren Baker ; Koustuv Sinha ; Carl Shneider ; Peter Henderson ; Joel Lehman ; Ryan Lowe
HIGHLIGHT:      The goal of the report is to disseminate these ideas more broadly, and in turn encourage continuing discussion about how the field could improve along these axes.

29, TITLE:      Fine-Grained Image Captioning with Global-Local Discriminative Objective
http://arxiv.org/abs/2007.10662
AUTHORS:        Jie Wu ; Tianshui Chen ; Hefeng Wu ; Zhi Yang ; Guangchun Luo ; Liang Lin
COMMENTS:       Accepted by TMM
HIGHLIGHT:      In this work, we propose a novel global-local discriminative objective that is formulated on top of a reference model to facilitate generating fine-grained descriptive captions.

30, TITLE:      Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
http://arxiv.org/abs/2007.10420
AUTHORS:        Kate Sanders ; Michael Danielczuk ; Jeffrey Mahler ; Ajay Tanwani ; Ken Goldberg
COMMENTS:       2020 IEEE International Conference on Automation Science and Engineering (CASE)
HIGHLIGHT:      Based on an empirical study of sequential failures, we characterize a class of "sequential failure objects" (SFOs) -- objects prone to sequential failures based on a novel taxonomy.

31, TITLE:      Fully Automated Segmentation of the Left Ventricle in Magnetic Resonance Images
http://arxiv.org/abs/2007.10665
AUTHORS:        ZiHao Wang ; ZhenZhou Wang
HIGHLIGHT:      In this paper, we try to reproduce the CNN based LV segmentation methods with their disclosed codes and trained CNN models.

32, TITLE:      Quantifying Performance Changes with Effect Size Confidence Intervals
http://arxiv.org/abs/2007.10899
AUTHORS:        Tomas Kalibera ; Richard Jones
COMMENTS:       A preliminary version of a portion of this work was presented at the Third European Performance Engineering Workshop
HIGHLIGHT:      Inspired by statistical methods used in other fields of science, and building on results in statistics that did not make it to introductory textbooks, we present a statistical model that allows us both to quantify uncertainty in the ratio of (execution time) means and to design experiments with a rigorous treatment of those multiple sources of non-determinism that might impact measured performance.

33, TITLE:      Integrative Analysis for COVID-19 Patient Outcome Prediction
http://arxiv.org/abs/2007.10416
AUTHORS:        Hanqing Chao ; Xi Fang ; Jiajin Zhang ; Fatemeh Homayounieh ; Chiara D. Arru ; Subba R. Digumarthy ; Rosa Babaei ; Hadi K. Mobin ; Iman Mohseni ; Luca Saba ; Alessandro Carriero ; Zeno Falaschi ; Alessio Pasche ; Ge Wang ; Mannudeep K. Kalra ; Pingkun Yan
HIGHLIGHT:      In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission.

34, TITLE:      Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery
http://arxiv.org/abs/2007.10891
AUTHORS:        Razieh Kaviani Baghbaderani ; Ying Qu ; Hairong Qi ; Craig Stutts
COMMENTS:       20 pages, 10 figures, European Conference on Computer Vision (ECCV) 2020
HIGHLIGHT:      In this paper, we study the problem of open-set land cover classification that identifies the samples belonging to unknown classes during testing, while maintaining performance on known classes.

35, TITLE:      PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
http://arxiv.org/abs/2007.10408
AUTHORS:        Zhengyang Shen ; Lingshen He ; Zhouchen Lin ; Jinwen Ma
COMMENTS:       Accepted by ICML2020
HIGHLIGHT:      In this work, we deal with this issue from the connection between convolutions and partial differential operators (PDOs).

36, TITLE:      Phase Transition Behavior in Knowledge Compilation
http://arxiv.org/abs/2007.10400
AUTHORS:        Rahul Gupta ; Subhajit Roy ; Kuldeep S. Meel
COMMENTS:       This is full version of the conference paper published at International Conference on Principles and Practice of Constraint Programming
HIGHLIGHT:      We perform a rigorous empirical study and analysis of the size and runtime behavior for different knowledge compilation forms (and their corresponding compilation algorithms): d-DNNFs, SDDs and OBDDs across multiple tools and compilation algorithms.

37, TITLE:      problemConquero at SemEval-2020 Task 12: Transformer and Soft label-based approaches
http://arxiv.org/abs/2007.10877
AUTHORS:        Karishma Laud ; Jagriti Singh ; Randeep Kumar Sahu ; Ashutosh Modi
COMMENTS:       10 pages,2 figures,8 tables, Accepted at Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020)
HIGHLIGHT:      In this paper, we present various systems submitted by our team problemConquero for SemEval-2020 Shared Task 12 Multilingual Offensive Language Identification in Social Media.

38, TITLE:      Distributed Memory based Self-Supervised Differentiable Neural Computer
http://arxiv.org/abs/2007.10637
AUTHORS:        Taewon Park ; Inchul Choi ; Minho Lee
HIGHLIGHT:      To address those issues, we propose a novel distributed memory-based self-supervised DNC architecture for enhanced memory augmented neural network performance.

39, TITLE:      Relative Pose Estimation for Multi-Camera Systems from Affine Correspondences
http://arxiv.org/abs/2007.10700
AUTHORS:        Banglei Guan ; Ji Zhao ; Daniel Barath ; Friedrich Fraundorfer
HIGHLIGHT:      Considering planar camera motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case.

40, TITLE:      Deep Preset: Blending and Retouching Photos with Color Style Transfer
http://arxiv.org/abs/2007.10701
AUTHORS:        Man M. Ho ; Jinjia Zhou
COMMENTS:       Our work is available at https://minhmanho.github.io/deep_preset
HIGHLIGHT:      Deep Preset: Blending and Retouching Photos with Color Style Transfer

41, TITLE:      XD at SemEval-2020 Task 12: Ensemble Approach to Offensive Language Identification in Social Media Using Transformer Encoders
http://arxiv.org/abs/2007.10945
AUTHORS:        Xiangjue Dong ; Jinho D. Choi
COMMENTS:       To be published in SemEval-2020
HIGHLIGHT:      This paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media.

42, TITLE:      Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos
http://arxiv.org/abs/2007.10703
AUTHORS:        Anurag Arnab ; Chen Sun ; Arsha Nagrani ; Cordelia Schmid
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we present a spatio-temporal action recognition model that is trained with only video-level labels, which are significantly easier to annotate.

43, TITLE:      Garment Design with Generative Adversarial Networks
http://arxiv.org/abs/2007.10947
AUTHORS:        Chenxi Yuan ; Mohsen Moghaddam
COMMENTS:       AdvML 2020, KDD workshop
HIGHLIGHT:      This paper explores the capabilities of generative adversarial networks (GAN) for automated attribute-level editing of design concepts.

44, TITLE:      MovieNet: A Holistic Dataset for Movie Understanding
http://arxiv.org/abs/2007.10937
AUTHORS:        Qingqiu Huang ; Yu Xiong ; Anyi Rao ; Jiaze Wang ; Dahua Lin
COMMENTS:       Accepted by ECCV2020 as spotlight presentation. Project page: http://movienet.site
HIGHLIGHT:      In this paper, we introduce MovieNet -- a holistic dataset for movie understanding.

45, TITLE:      Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation
http://arxiv.org/abs/2007.10787
AUTHORS:        Yanning Zhou ; Hao Chen ; Huangjing Lin ; Pheng-Ann Heng
COMMENTS:       to appear at MICCAI2020, supplementary material attached
HIGHLIGHT:      In this paper, we propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation.

46, TITLE:      Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
http://arxiv.org/abs/2007.10930
AUTHORS:        David Klindt ; Lukas Schott ; Yash Sharma ; Ivan Ustyuzhaninov ; Wieland Brendel ; Matthias Bethge ; Dylan Paiton
COMMENTS:       Code is available at https://github.com/bethgelab/slow_disentanglement; The first three authors, as well as the last two authors, contributed equally
HIGHLIGHT:      We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos.

47, TITLE:      Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
http://arxiv.org/abs/2007.10778
AUTHORS:        Xian Zhong ; Cheng Gu ; Wenxin Huang ; Lin Li ; Shuqin Chen ; Chia-Wen Lin
COMMENTS:       25th International Conference on Pattern Recognition (ICPR2020)
HIGHLIGHT:      Aiming at learning better representations, we propose a meta-learning approach with complemented representations network (MCRNet) for few-shot image classification.

48, TITLE:      Partial Boolean functions with exact quantum 1-query complexity
http://arxiv.org/abs/2007.10924
AUTHORS:        Guoliang Xu ; Daowen Qiu
COMMENTS:       11pages; comments are welcome
HIGHLIGHT:      Using the first characterization, we present all $n$-bit partial Boolean functions that depend on $n$ bits and have exact quantum 1-query complexity.

49, TITLE:      Regularizing Deep Networks with Semantic Data Augmentation
http://arxiv.org/abs/2007.10538
AUTHORS:        Yulin Wang ; Gao Huang ; Shiji Song ; Xuran Pan ; Yitong Xia ; Cheng Wu
COMMENTS:       Code is available at https://github.com/blackfeather-wang/ISDA-for-Deep-Networks. arXiv admin note: substantial text overlap with arXiv:1909.12220
HIGHLIGHT:      To this end, we propose a novel semantic data augmentation algorithm to complement traditional approaches.

50, TITLE:      What is important about the No Free Lunch theorems?
http://arxiv.org/abs/2007.10928
AUTHORS:        David H. Wolpert
COMMENTS:       15 pages, 11 of main text, to be published in "Black Box Optimization, Machine Learning and No-Free Lunch Theorems", P. Pardalos, V. Rasskazova, M.N. Vrahatis, Ed., Springer
HIGHLIGHT:      As I discuss in this chapter, the importance of the theorems arises by using them to analyze scenarios involving {non-uniform} distributions, and to compare different algorithms, without any assumption about the distribution over problems at all.

51, TITLE:      Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features
http://arxiv.org/abs/2007.10534
AUTHORS:        Gullal S. Cheema ; Sherzod Hakimov ; Ralph Ewerth
COMMENTS:       CLEF2020-CheckThat!
HIGHLIGHT:      In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media.

52, TITLE:      Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
http://arxiv.org/abs/2007.10527
AUTHORS:        Sachin Ravi ; Sebastian Musslick ; Maia Hamin ; Theodore L. Willke ; Jonathan D. Cohen
HIGHLIGHT:      We show that the same tension arises in deep networks and discuss a meta-learning algorithm for an agent to manage this trade-off in an unfamiliar environment.

53, 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 ; Anmin Fu ; Surya Nepal ; Hyoungshick Kim
COMMENTS:       28 pages, 9 figures, 2 tables
HIGHLIGHT:      This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning.

54, TITLE:      DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation
http://arxiv.org/abs/2007.10521
AUTHORS:        Saeed Khaki ; Hieu Pham ; Ye Han ; Andy Kuhl ; Wade Kent ; Lizhi Wang
COMMENTS:       19 pages, 6 figures
HIGHLIGHT:      In this paper, we propose a novel deep learning method for counting on-ear corn kernels in-field to aid in the gathering of real-time data and, ultimately, to improve decision making to maximize yield.

55, TITLE:      NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search
http://arxiv.org/abs/2007.10396
AUTHORS:        Zhichao Lu ; Kalyanmoy Deb ; Erik Goodman ; Wolfgang Banzhaf ; Vishnu Naresh Boddeti
COMMENTS:       Accepted for oral presentation at ECCV 2020
HIGHLIGHT:      In this paper, we propose an efficient NAS algorithm for generating task-specific models that are competitive under multiple competing objectives.

56, TITLE:      Translation Between Waves, wave2wave
http://arxiv.org/abs/2007.10394
AUTHORS:        Tsuyoshi Okita ; Hirotaka Hachiya ; Sozo Inoue ; Naonori Ueda
HIGHLIGHT:      This paper proposes a new variant of neural machine translation seq2seq to deal with continuous signal waves by introducing the window-based (inverse-) representation to adaptively represent partial shapes of waves and the iterative back-translation model for high-dimensional data.

57, TITLE:      Enhancement of damaged-image prediction through Cahn-Hilliard Image Inpainting
http://arxiv.org/abs/2007.10753
AUTHORS:        José A. Carrillo ; Serafim Kalliadasis ; Fuyue Liang ; Sergio P. Perez
COMMENTS:       The code supporting this work is available at https://github.com/fuyueliang/Enhanced-Image-prediction-through-Cahn-Hilliard-image-inpainiting- The MNIST dataset employed in this work can be downloaded from http://yann.lecun.com/exdb/mnist/
HIGHLIGHT:      For this we employ a modified Cahn-Hilliard equation as an image inpainting filter, which is solved via a finite volume scheme with reduced computational cost and adequate properties for energy stability and boundedness.

58, TITLE:      Multi-modal Transformer for Video Retrieval
http://arxiv.org/abs/2007.10639
AUTHORS:        Valentin Gabeur ; Chen Sun ; Karteek Alahari ; Cordelia Schmid
COMMENTS:       ECCV 2020 (spotlight paper)
HIGHLIGHT:      In this paper, we present a multi-modal transformer to jointly encode the different modalities in video, which allows each of them to attend to the others.

59, TITLE:      Generative Hierarchical Features from Synthesizing Images
http://arxiv.org/abs/2007.10379
AUTHORS:        Yinghao Xu ; Yujun Shen ; Jiapeng Zhu ; Ceyuan Yang ; Bolei Zhou
COMMENTS:       15 pages, 13 figures, 2 tables
HIGHLIGHT:      In this work, we show that learning to synthesize images is able to bring remarkable hierarchical visual features that are generalizable across a wide range of visual tasks.

60, TITLE:      Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry
http://arxiv.org/abs/2007.10986
AUTHORS:        He Chen ; Pengfei Guo ; Pengfei Li ; Gim Hee Lee ; Gregory Chirikjian
HIGHLIGHT:      In this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation.

61, TITLE:      Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
http://arxiv.org/abs/2007.10872
AUTHORS:        Jianfeng Yan ; Zizhuang Wei ; Hongwei Yi ; Mingyu Ding ; Runze Zhang ; Yisong Chen ; Guoping Wang ; Yu-Wing Tai
COMMENTS:       Accepted by ECCV2020 as Spotlight
HIGHLIGHT:      In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely $D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction.

62, TITLE:      Connecting Embeddings for Knowledge Graph Entity Typing
http://arxiv.org/abs/2007.10873
AUTHORS:        Yu Zhao ; Anxiang Zhang ; Ruobing Xie ; Kang Liu ; Xiaojie Wang
HIGHLIGHT:      In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge from KGs.

63, TITLE:      Learning Person Re-identification Models from Videos with Weak Supervision
http://arxiv.org/abs/2007.10631
AUTHORS:        Xueping Wang ; Sujoy Paul ; Dripta S. Raychaudhuri ; Min Liu ; Yaonan Wang ; Amit K. Roy-Chowdhury ;  Fellow ;  IEEE
HIGHLIGHT:      In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision.

64, TITLE:      Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop
http://arxiv.org/abs/2007.10504
AUTHORS:        Jonathan Chung ; Anna Luo ; Xavier Raffin ; Scott Perry
HIGHLIGHT:      We present the Battlesnake Challenge, a framework for multi-agent reinforcement learning with Human-In-the-Loop Learning (HILL).

65, TITLE:      Analysis and Optimization of Service Delay for Multi-quality Videos in Multi-tier Heterogeneous Network with Random Caching
http://arxiv.org/abs/2007.10633
AUTHORS:        Xuewei Zhang ; Tiejun Lv ; Yuan Ren ; Wei Ni ; Norman C. Beaulieu
COMMENTS:       13 pages, 8 figures, IEEE Systems Journal, Accepted
HIGHLIGHT:      Aiming to minimize service delay, we propose a new random caching scheme in device-to-device (D2D)-assisted heterogeneous network.

66, TITLE:      IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals
http://arxiv.org/abs/2007.10866
AUTHORS:        Anirudh Anil Ojha ; Rohin Garg ; Shashank Gupta ; Ashutosh Modi
COMMENTS:       10 pages, 1 figure, 4 tables. For associated code, see https://github.com/gargrohin/Counterfactuals-NLP. Accepted at Proceedings of 14th International Workshop on Semantic Evaluation (SemEval-2020)
HIGHLIGHT:      This paper describes our efforts in tackling Task 5 of SemEval-2020.

67, TITLE:      Sparse Nonnegative Tensor Factorization and Completion with Noisy Observations
http://arxiv.org/abs/2007.10626
AUTHORS:        Xiongjun Zhang ; Michael K. Ng
HIGHLIGHT:      In this paper, we study the sparse nonnegative tensor factorization and completion problem from partial and noisy observations for third-order tensors.

68, TITLE:      SLNSpeech: solving extended speech separation problem by the help of sign language
http://arxiv.org/abs/2007.10629
AUTHORS:        Jiasong Wu ; Taotao Li ; Youyong Kong ; Guanyu Yang ; Lotfi Senhadji ; Huazhong Shu
COMMENTS:       33 pages, 8 figures, 5 tables
HIGHLIGHT:      In order to make speech separation technology applied in the real scenario of the disabled, this paper presents an extended speech separation problem which refers in particular to sign language assisted speech separation. To address the extended speech separation problem, we introduce a large-scale dataset named Sign Language News Speech (SLNSpeech) dataset in which three modalities of audio, visual, and sign language are coexisted.

69, TITLE:      Towards Visual Distortion in Black-Box Attacks
http://arxiv.org/abs/2007.10593
AUTHORS:        Nannan Li ; Zhenzhong Chen
HIGHLIGHT:      In this paper, we propose a novel black-box attack approach that can directly minimize the induced distortion by learning the noise distribution of the adversarial example, assuming only loss-oracle access to the black-box network.

70, TITLE:      Video Super-resolution with Temporal Group Attention
http://arxiv.org/abs/2007.10595
AUTHORS:        Takashi Isobe ; Songjiang Li ; Xu Jia ; Shanxin Yuan ; Gregory Slabaugh ; Chunjing Xu ; Ya-Li Li ; Shengjin Wang ; Qi Tian
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we propose a novel method that can effectively incorporate temporal information in a hierarchical way.

71, TITLE:      Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification
http://arxiv.org/abs/2007.10854
AUTHORS:        Jianing Li ; Shiliang Zhang
HIGHLIGHT:      This paper tackles this challenge through jointly enforcing visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification.

72, TITLE:      Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement
http://arxiv.org/abs/2007.10599
AUTHORS:        Jian Wang ; Xiang Long ; Yuan Gao ; Errui Ding ; Shilei Wen
COMMENTS:       Accepted to ECCV2020
HIGHLIGHT:      In this paper, we aim to find a better approach to get more accurate localization results.

73, TITLE:      Detection, Attribution and Localization of GAN Generated Images
http://arxiv.org/abs/2007.10466
AUTHORS:        Michael Goebel ; Lakshmanan Nataraj ; Tejaswi Nanjundaswamy ; Tajuddin Manhar Mohammed ; Shivkumar Chandrasekaran ; B. S. Manjunath
HIGHLIGHT:      In this paper, we propose a novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods.

74, TITLE:      Second-Order Pooling for Graph Neural Networks
http://arxiv.org/abs/2007.10467
AUTHORS:        Zhengyang Wang ; Shuiwang Ji
COMMENTS:       12 pages, 2 figures, https://www.computer.org/csdl/journal/tp/5555/01/09104936/1kj0O2A1yBa
HIGHLIGHT:      In this work, we propose to use second-order pooling as graph pooling, which naturally solves the above challenges.

75, 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.

76, TITLE:      Smoothed Complexity of 2-player Nash Equilibria
http://arxiv.org/abs/2007.10857
AUTHORS:        Shant Boodaghians ; Joshua Brakensiek ; Samuel B. Hopkins ; Aviad Rubinstein
COMMENTS:       21 pages, 1 figure; FOCS 2020
HIGHLIGHT:      We prove that computing a Nash equilibrium of a two-player ($n \times n$) game with payoffs in $[-1,1]$ is PPAD-hard (under randomized reductions) even in the smoothed analysis setting, smoothing with noise of constant magnitude.

77, TITLE:      Multi-label Thoracic Disease Image Classification with Cross-Attention Networks
http://arxiv.org/abs/2007.10859
AUTHORS:        Congbo Ma ; Hu Wang ; Steven C. H. Hoi
HIGHLIGHT:      To overcome these challenges, in this paper, we propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images, which can effectively excavate more meaningful representation from data to boost the performance through cross-attention by only image-level annotations.

78, TITLE:      Novel View Synthesis on Unpaired Data by Conditional Deformable Variational Auto-Encoder
http://arxiv.org/abs/2007.10618
AUTHORS:        Mingyu Yin ; Li Sun ; Qingli Li
COMMENTS:       ECCV 2020
HIGHLIGHT:      This paper proposes a view translation model under cVAE-GAN framework without requiring the paired data.

79, TITLE:      Word Representation for Rhythms
http://arxiv.org/abs/2007.10610
AUTHORS:        Tongyu Lu ; Lucheng Yan ; Gus Xia
COMMENTS:       5 pages, 8 figures
HIGHLIGHT:      This paper proposes a word representation strategy for rhythm patterns.

80, TITLE:      Points2Surf: Learning Implicit Surfaces from Point Cloud Patches
http://arxiv.org/abs/2007.10453
AUTHORS:        Philipp Erler ; Paul Guerrero ; Stefan Ohrhallinger ; Michael Wimmer ; Niloy J. Mitra
COMMENTS:       To be published at ECCV 2020 Repository: https://github.com/ErlerPhilipp/points2surf
HIGHLIGHT:      We present Points2Surf, a novel patch-based learning framework that produces accurate surfaces directly from raw scans without normals.

81, TITLE:      Feature-metric Loss for Self-supervised Learning of Depth and Egomotion
http://arxiv.org/abs/2007.10603
AUTHORS:        Chang Shu ; Kun Yu ; Zhixiang Duan ; Kuiyuan Yang
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      In this work, feature-metric loss is proposed and defined on feature representation, where the feature representation is also learned in a self-supervised manner and regularized by both first-order and second-order derivatives to constrain the loss landscapes to form proper convergence basins.

82, TITLE:      Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense
http://arxiv.org/abs/2007.10457
AUTHORS:        Sailik Sengupta ; Subbarao Kambhampati
HIGHLIGHT:      To take away an attacker's advantage of reconnaissance, researchers have proposed proactive defense methods such as Moving Target Defense (MTD).

83, TITLE:      A Deep Ordinal Distortion Estimation Approach for Distortion Rectification
http://arxiv.org/abs/2007.10689
AUTHORS:        Kang Liao ; Chunyu Lin ; Yao Zhao
HIGHLIGHT:      In this work, we propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency.

84, TITLE:      Neural Machine Translation with Error Correction
http://arxiv.org/abs/2007.10681
AUTHORS:        Kaitao Song ; Xu Tan ; Jianfeng Lu
COMMENTS:       Accepted by IJCAI 2020
HIGHLIGHT:      In this paper, we introduce an error correction mechanism into NMT, which corrects the error information in the previous generated tokens to better predict the next token.

85, TITLE:      Soft Expert Reward Learning for Vision-and-Language Navigation
http://arxiv.org/abs/2007.10835
AUTHORS:        Hu Wang ; Qi Wu ; Chunhua Shen
HIGHLIGHT:      In this paper, we introduce a Soft Expert Reward Learning (SERL) model to overcome the reward engineering designing and generalisation problems of the VLN task.

86, TITLE:      Unlocking the Potential of Deep Counterfactual Value Networks
http://arxiv.org/abs/2007.10442
AUTHORS:        Ryan Zarick ; Bryan Pellegrino ; Noam Brown ; Caleb Banister
COMMENTS:       11 pages, 6 figures
HIGHLIGHT:      In this paper we introduce several improvements to deep counterfactual value networks, as well as counterfactual regret minimization, and analyze the effects of each change.

87, TITLE:      CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE
http://arxiv.org/abs/2007.10830
AUTHORS:        Soumya Ranjan Dash ; Sandeep Routray ; Prateek Varshney ; Ashutosh Modi
COMMENTS:       6 pages, 2 figures, 2 tables Accepted at Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020)
HIGHLIGHT:      In this paper, we describe our system for Task 4 of SemEval 2020, which involves differentiating between natural language statements that confirm to common sense and those that do not.

88, TITLE:      IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet Memes
http://arxiv.org/abs/2007.10822
AUTHORS:        Vishal Keswani ; Sakshi Singh ; Suryansh Agarwal ; Ashutosh Modi
COMMENTS:       7 pages, 2 figures, 3 tables. Accepted at Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020)
HIGHLIGHT:      In this paper, we present our approaches for the Memotion Analysis problem as posed in SemEval-2020 Task 8.

89, TITLE:      What Programs Want: Automatic Inference of Input Data Specifications
http://arxiv.org/abs/2007.10688
AUTHORS:        Caterina Urban
HIGHLIGHT:      In this paper, we propose a static shape analysis framework for input data of data-processing programs.

90, TITLE:      newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations For Propaganda Classification
http://arxiv.org/abs/2007.10827
AUTHORS:        Paramansh Singh ; Siraj Sandhu ; Subham Kumar ; Ashutosh Modi
COMMENTS:       7 pages, 4 figures, 2 tables Accepted at Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020)
HIGHLIGHT:      This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification.

91, TITLE:      Sorted Pooling in Convolutional Networks for One-shot Learning
http://arxiv.org/abs/2007.10495
AUTHORS:        András Horváth
COMMENTS:       Old paper submitted to ECCV 2018
HIGHLIGHT:      We present generalized versions of the commonly used maximum pooling operation: $k$th maximum and sorted pooling operations which selects the $k$th largest response in each pooling region, selecting locally consistent features of the input images.

92, TITLE:      CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks
http://arxiv.org/abs/2007.10497
AUTHORS:        Shayan Hassantabar ; Novati Stefano ; Vishweshwar Ghanakota ; Alessandra Ferrari ; Gregory N. Nicola ; Raffaele Bruno ; Ignazio R. Marino ; Niraj K. Jha
COMMENTS:       11 pages, 3 figures
HIGHLIGHT:      In this work, we propose CovidDeep, a framework that combines efficient DNNs with commercially available WMSs for pervasive testing of the coronavirus. We collected data from 87 individuals, spanning four cohorts including healthy, asymptomatic (but SARS-CoV-2-positive) as well as moderately and severely symptomatic COVID-19 patients.

93, TITLE:      Quantum and Classical Hybrid Generations for Classical Correlations
http://arxiv.org/abs/2007.10673
AUTHORS:        Xiaodie Lin ; Zhaohui Wei ; Penghui Yao
COMMENTS:       13 pages
HIGHLIGHT:      We consider two-stage hybrid protocols that combine quantum resource and classical resource to generate classical correlations shared by two separated players.

94, TITLE:      Deep vs. Deep Bayesian: Reinforcement Learning on a Multi-Robot Competitive Experiment
http://arxiv.org/abs/2007.10675
AUTHORS:        Jingyi Huang ; Andre Rosendo
HIGHLIGHT:      Deep vs. Deep Bayesian: Reinforcement Learning on a Multi-Robot Competitive Experiment

95, TITLE:      AdvFoolGen: Creating Persistent Troubles for Deep Classifiers
http://arxiv.org/abs/2007.10485
AUTHORS:        Yuzhen Ding ; Nupur Thakur ; Baoxin Li
COMMENTS:       11 pages, 5 figures
HIGHLIGHT:      In this paper, we present a new black-box attack termed AdvFoolGen, which can generate attacking images from the same feature space as that of the natural images, so as to keep baffling the network even though state-of-the-art defense mechanisms have been applied.

96, TITLE:      Conformer-Kernel with Query Term Independence for Document Retrieval
http://arxiv.org/abs/2007.10434
AUTHORS:        Bhaskar Mitra ; Sebastian Hofstatter ; Hamed Zamani ; Nick Craswell
HIGHLIGHT:      In this work, we extend the TK architecture to the full retrieval setting by incorporating the query term independence assumption.

97, TITLE:      An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models
http://arxiv.org/abs/2007.10668
AUTHORS:        Catarina Moreira ; Yu-Liang Chou ; Mythreyi Velmurugan ; Chun Ouyang ; Renuka Sindhgatta ; Peter Bruza
HIGHLIGHT:      In this paper, we propose a novel approach underpinned by an extended framework of Bayesian networks for generating post hoc interpretations of a black-box predictive model.

98, TITLE:      Active MR k-space Sampling with Reinforcement Learning
http://arxiv.org/abs/2007.10469
AUTHORS:        Luis Pineda ; Sumana Basu ; Adriana Romero ; Roberto Calandra ; Michal Drozdzal
COMMENTS:       To appear in 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
HIGHLIGHT:      In this paper, we focus on learning acquisition trajectories given a fixed image reconstruction model.



==========Updates to Previous Papers==========
1, TITLE:       FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method
http://arxiv.org/abs/2007.09510
AUTHORS:        Mozhdeh Rouhsedaghat ; Yifan Wang ; Xiou Ge ; Shuowen Hu ; Suya You ; C. -C. Jay Kuo
HIGHLIGHT:      A light-weight low-resolution face gender classification method, called FaceHop, is proposed in this research.

2, TITLE:       Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation
http://arxiv.org/abs/1912.03001
AUTHORS:        Hongwei Yi ; Zizhuang Wei ; Mingyu Ding ; Runze Zhang ; Yisong Chen ; Guoping Wang ; Yu-Wing Tai
COMMENTS:       Accepted by ECCV2020 as a Poster
HIGHLIGHT:      n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction.

3, TITLE:       Deep Reinforcement Learning for Cyber Security
http://arxiv.org/abs/1906.05799
AUTHORS:        Thanh Thi Nguyen ; Vijay Janapa Reddi
HIGHLIGHT:      This paper presents a survey of DRL approaches developed for cyber security.

4, TITLE:       Understanding Dynamic Scenes using Graph Convolution Networks
http://arxiv.org/abs/2005.04437
AUTHORS:        Sravan Mylavarapu ; Mahtab Sandhu ; Priyesh Vijayan ; K Madhava Krishna ; Balaraman Ravindran ; Anoop Namboodiri
COMMENTS:       To appear at IROS 2020
HIGHLIGHT:      We present a novel Multi Relational Graph Convolutional Network (MRGCN) to model on-road vehicle behaviours from a sequence of temporally ordered frames as grabbed by a moving monocular camera.

5, TITLE:       Scene Text Image Super-Resolution in the Wild
http://arxiv.org/abs/2005.03341
AUTHORS:        Wenjia Wang ; Enze Xie ; Xuebo Liu ; Wenhai Wang ; Ding Liang ; Chunhua Shen ; Xiang Bai
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      In this purpose, a new Text Super-Resolution Network termed TSRN, with three novel modules is developed.

6, TITLE:       Building Information Modeling and Classification by Visual Learning At A City Scale
http://arxiv.org/abs/1910.06391
AUTHORS:        Qian Yu ; Chaofeng Wang ; Barbaros Cetiner ; Stella X. Yu ; Frank Mckenna ; Ertugrul Taciroglu ; Kincho H. Law
COMMENTS:       33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
HIGHLIGHT:      In this paper, we provide two case studies to demonstrate how artificial intelligence can empower civil engineering. We also create a large-scale building image database and a semi-automated image labeling approach that effectively annotates new database entries.

7, TITLE:       Computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model of Medical Disagreement
http://arxiv.org/abs/2007.00709
AUTHORS:        Hossein Hematialam ; Luciana Garbayo ; Seethalakshmi Gopalakrishnan ; Wlodek Zadrozny
COMMENTS:       39 pages, 4 figures
HIGHLIGHT:      Using natural language processing tools, we investigate the differences of recommendations in medical guidelines for the same decision problem -- breast cancer screening.

8, 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
COMMENTS:       meta-learing,few-shot detection
HIGHLIGHT:      Therefore, for meta-learning method of few-shot detection, we propose a TCL which exploits the true-label example and the most similar semantics with the example, and a category-based grouping mechanism which groups categories by appearance and environment to enhance the semantic features between similar categories.

9, TITLE:       Making Robots Draw A Vivid Portrait In Two Minutes
http://arxiv.org/abs/2005.05526
AUTHORS:        Fei Gao ; Jingjie Zhu ; Zeyuan Yu ; Peng Li ; Tao Wang
COMMENTS:       7 pages, 7 figures; accepted by IROS2020
HIGHLIGHT:      Besides, we propose a componential sparsity constraint to reduce the number of brush-strokes over insignificant areas.

10, TITLE:      ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
http://arxiv.org/abs/2007.06225
AUTHORS:        Ahmed Elnaggar ; Michael Heinzinger ; Christian Dallago ; Ghalia Rihawi ; Yu Wang ; Llion Jones ; Tom Gibbs ; Tamas Feher ; Christoph Angerer ; Martin Steinegger ; Debsindhu Bhowmik ; Burkhard Rost
HIGHLIGHT:      Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia).

11, TITLE:      GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy
http://arxiv.org/abs/2007.09968
AUTHORS:        Shaoteng Liu ; Lijun Gong ; Kai Ma ; Yefeng Zheng
COMMENTS:       MICCAI2020
HIGHLIGHT:      In this paper, we propose a Graph REsidual rE-ranking Network (GREEN) to introduce a class dependency prior into the original image classification network.

12, TITLE:      Intra-clip Aggregation for Video Person Re-identification
http://arxiv.org/abs/1905.01722
AUTHORS:        Takashi Isobe ; Jian Han ; Fang Zhu ; Yali Li ; Shengjin Wang
COMMENTS:       ICIP 2020
HIGHLIGHT:      Intra-clip Aggregation for Video Person Re-identification

13, TITLE:      Prototypical Contrastive Learning of Unsupervised Representations
http://arxiv.org/abs/2005.04966
AUTHORS:        Junnan Li ; Pan Zhou ; Caiming Xiong ; Richard Socher ; Steven C. H. Hoi
HIGHLIGHT:      This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning.

14, TITLE:      Environment-agnostic Multitask Learning for Natural Language Grounded Navigation
http://arxiv.org/abs/2003.00443
AUTHORS:        Xin Eric Wang ; Vihan Jain ; Eugene Ie ; William Yang Wang ; Zornitsa Kozareva ; Sujith Ravi
COMMENTS:       ECCV 2020
HIGHLIGHT:      To close the gap between seen and unseen environments, we aim at learning a generalized navigation model from two novel perspectives: (1) we introduce a multitask navigation model that can be seamlessly trained on both Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks, which benefits from richer natural language guidance and effectively transfers knowledge across tasks; (2) we propose to learn environment-agnostic representations for the navigation policy that are invariant among the environments seen during training, thus generalizing better on unseen environments.

15, TITLE:      Learning to Generate Customized Dynamic 3D Facial Expressions
http://arxiv.org/abs/2007.09805
AUTHORS:        Rolandos Alexandros Potamias ; Jiali Zheng ; Stylianos Ploumpis ; Giorgos Bouritsas ; Evangelos Ververas ; Stefanos Zafeiriou
COMMENTS:       accepted at European Conference on Computer Vision 2020 (ECCV)
HIGHLIGHT:      In this paper, we extrapolate those advances to the 3D domain, by studying 3D image-to-video translation with a particular focus on 4D facial expressions.

16, TITLE:      AlignNet: Unsupervised Entity Alignment
http://arxiv.org/abs/2007.08973
AUTHORS:        Antonia Creswell ; Kyriacos Nikiforou ; Oriol Vinyals ; Andre Saraiva ; Rishabh Kabra ; Loic Matthey ; Chris Burgess ; Malcolm Reynolds ; Richard Tanburn ; Marta Garnelo ; Murray Shanahan
HIGHLIGHT:      In this paper we take steps towards solving the alignment problem, presenting the AlignNet, an unsupervised alignment module.

17, TITLE:      Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures
http://arxiv.org/abs/2007.08970
AUTHORS:        Daniel Furrer ; Marc van Zee ; Nathan Scales ; Nathanael Schärli
HIGHLIGHT:      We show that masked language model (MLM) pre-training rivals SCAN-inspired architectures on primitive holdout splits.

18, TITLE:      Event Prediction in Big Data Era: A Systematic Survey
http://arxiv.org/abs/2007.09815
AUTHORS:        Liang Zhao
HIGHLIGHT:      This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era.

19, TITLE:      Lightweight image super-resolution with enhanced CNN
http://arxiv.org/abs/2007.04344
AUTHORS:        Chunwei Tian ; Ruibin Zhuge ; Zhihao Wu ; Yong Xu ; Wangmeng Zuo ; Chen Chen ; Chia-Wen Lin
HIGHLIGHT:      To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB).

20, TITLE:      PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
http://arxiv.org/abs/2003.03808
AUTHORS:        Sachit Menon ; Alexandru Damian ; Shijia Hu ; Nikhil Ravi ; Cynthia Rudin
COMMENTS:       Sachit Menon and Alexandru Damian contributed equally. Computer Vision and Pattern Recognition (CVPR) 2020
HIGHLIGHT:      We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.

21, TITLE:      Object-Centric Multi-View Aggregation
http://arxiv.org/abs/2007.10300
AUTHORS:        Shubham Tulsiani ; Or Litany ; Charles R. Qi ; He Wang ; Leonidas J. Guibas
HIGHLIGHT:      We present an approach for aggregating a sparse set of views of an object in order to compute a semi-implicit 3D representation in the form of a volumetric feature grid.

22, TITLE:      Decentralized MCTS via Learned Teammate Models
http://arxiv.org/abs/2003.08727
AUTHORS:        Aleksander Czechowski ; Frans A. Oliehoek
COMMENTS:       Sole copyright holder is IJCAI, all rights reserved. Published version available online: https://doi.org/10.24963/ijcai.2020/12
HIGHLIGHT:      In this paper, we present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search, combined with models of teammates learned from previous episodic runs.

23, TITLE:      A Framework for Building Closed-Domain Chat Dialogue Systems
http://arxiv.org/abs/1910.13826
AUTHORS:        Mikio Nakano ; Kazunori Komatani
COMMENTS:       24 pages
HIGHLIGHT:      This paper presents HRIChat, a framework for developing closed-domain chat dialogue systems.

24, TITLE:      Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
http://arxiv.org/abs/2004.11676
AUTHORS:        Narinder Singh Punn ; Sonali Agarwal
HIGHLIGHT:      Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images.

25, TITLE:      Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
http://arxiv.org/abs/2004.09363
AUTHORS:        Shervin Minaee ; Rahele Kafieh ; Milan Sonka ; Shakib Yazdani ; Ghazaleh Jamalipour Soufi
COMMENTS:       Accepted by Medical Image Analysis
HIGHLIGHT:      In this work, we propose a model based on sentence Transformer to detect the main topics of Tweets in recent months.

26, TITLE:      Scientific Discourse Tagging for Evidence Extraction
http://arxiv.org/abs/1909.04758
AUTHORS:        Xiangci Li ; Gully Burns ; Nanyun Peng
COMMENTS:       7 pages of main texts, 2 pages of references and 1 page of supportive information. 8 figures and 5 tables
HIGHLIGHT:      We present the capability of automatically extracting text fragments from primary research papers that describe the evidence presented in that paper's figures, which arguably provides the raw material of any scientific argument made within the paper.

27, TITLE:      Asymptotically Unambitious Artificial General Intelligence
http://arxiv.org/abs/1905.12186
AUTHORS:        Michael K Cohen ; Badri Vellambi ; Marcus Hutter
COMMENTS:       9 pages with 5 figures; 10 page Appendix with 2 figures
HIGHLIGHT:      We present the first algorithm we are aware of for asymptotically unambitious AGI, where "unambitiousness" includes not seeking arbitrary power.

28, TITLE:      Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
http://arxiv.org/abs/1901.07261
AUTHORS:        Xiangxiang Chu ; Bo Zhang ; Hailong Ma ; Ruijun Xu ; Qingyuan Li
COMMENTS:       Accepted to ICPR20
HIGHLIGHT:      Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search.

29, TITLE:      PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
http://arxiv.org/abs/2004.13513
AUTHORS:        Arthur Douillard ; Matthieu Cord ; Charles Ollion ; Thomas Robert ; Eduardo Valle
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      In this work, we propose PODNet, a model inspired by representation learning.

30, TITLE:      Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance
http://arxiv.org/abs/2007.06936
AUTHORS:        Marvin Klingner ; Jan-Aike Termöhlen ; Jonas Mikolajczyk ; Tim Fingscheidt
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this work we present a new self-supervised semantically-guided depth estimation (SGDepth) method to deal with moving dynamic-class (DC) objects, such as moving cars and pedestrians, which violate the static-world assumptions typically made during training of such models.

31, TITLE:      REPrune: Filter Pruning via Representative Election
http://arxiv.org/abs/2007.06932
AUTHORS:        Mincheol Park ; Woojeong Kim ; Suhyun Kim
COMMENTS:       Under Review at ECCV 2020
HIGHLIGHT:      Our novel pruning method entitled "REPrune" addresses this problem by selecting representative filters via clustering.

32, TITLE:      Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
http://arxiv.org/abs/2007.01259
AUTHORS:        Hui Xie ; Zhe Pan ; Leixin Zhou ; Fahim A Zaman ; Danny Chen ; Jost B Jonas ; Yaxing Wang ; Xiaodong Wu
COMMENTS:       11 pages main content and reference, plus 10 pages appendix, total 21 pages
HIGHLIGHT:      In this work, we propose to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters.

33, TITLE:      Novel-View Human Action Synthesis
http://arxiv.org/abs/2007.02808
AUTHORS:        Mohamed Ilyes Lakhal ; Davide Boscaini ; Fabio Poiesi ; Oswald Lanz ; Andrea Cavallaro
HIGHLIGHT:      We present a novel 3D reasoning to synthesize the target viewpoint.

34, TITLE:      Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications
http://arxiv.org/abs/2007.09979
AUTHORS:        Lijun Gong ; Kai Ma ; Yefeng Zheng
COMMENTS:       MICCAI2020
HIGHLIGHT:      In this paper, we explore distractors from the CNN feature space via proposing a neuron intrinsic learning method.

35, TITLE:      Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning
http://arxiv.org/abs/1904.12907
AUTHORS:        Haonan Chen ; Hao Tan ; Alan Kuntz ; Mohit Bansal ; Ron Alterovitz
COMMENTS:       7 pages, 4 figures, ICRA 2020
HIGHLIGHT:      In this paper, we introduce Language-Model-based Commonsense Reasoning (LMCR), a new method which enables a robot to listen to a natural language instruction from a human, observe the environment around it, and automatically fill in information missing from the instruction using environmental context and a new commonsense reasoning approach.

36, TITLE:      Computing regular meromorphic differential forms via Saito's logarithmic residues
http://arxiv.org/abs/2007.09950
AUTHORS:        Shinichi Tajima ; Katsusuke Nabeshima
HIGHLIGHT:      An effective method is introduced for computing logarithmic residues.

37, 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 $\ps$ with relaxed accesses ($\psr$) together with promises.

38, TITLE:      AnyHLS: High-Level Synthesis with Partial Evaluation
http://arxiv.org/abs/2002.05796
AUTHORS:        M. Akif Özkan ; Arsène Pérard-Gayot ; Richard Membarth ; Philipp Slusallek ; Roland Leissa ; Sebastian Hack ; Jürgen Teich ; Frank Hannig
COMMENTS:       12 pages, 9 figures
HIGHLIGHT:      In this paper, we present AnyHLS, an approach to synthesize FPGA designs in a modular and abstract way.

39, TITLE:      MOReL : Model-Based Offline Reinforcement Learning
http://arxiv.org/abs/2005.05951
AUTHORS:        Rahul Kidambi ; Aravind Rajeswaran ; Praneeth Netrapalli ; Thorsten Joachims
COMMENTS:       First two authors contributed equally
HIGHLIGHT:      In this work, we present MOReL, an algorithmic framework for model-based offline RL.

40, TITLE:      3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection
http://arxiv.org/abs/2004.12636
AUTHORS:        Jin Hyeok Yoo ; Yecheol Kim ; Jisong Kim ; Jun Won Choi
HIGHLIGHT:      In this paper, we propose a new deep architecture for fusing camera and LiDAR sensors for 3D object detection.

41, TITLE:      NPCFace: A Negative-Positive Cooperation Supervision for Training Large-scale Face Recognition
http://arxiv.org/abs/2007.10172
AUTHORS:        Dan Zeng ; Hailin Shi ; Hang Du ; Jun Wang ; Zhen Lei ; Tao Mei
HIGHLIGHT:      In this paper, we study how to make better use of these hard samples for improving the training.

42, TITLE:      Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition
http://arxiv.org/abs/1911.07335
AUTHORS:        Haw-Shiuan Chang ; Shankar Vembu ; Sunil Mohan ; Rheeya Uppaal ; Andrew McCallum
COMMENTS:       This is a pre-print of an article published in Springer Machine Learning journal. The final authenticated version is available online at: https://doi.org/10.1007/s10994-020-05897-1
HIGHLIGHT:      In response, we propose a transparent batch active sampling framework by estimating the error decay curves of multiple feature-defined subsets of the data.

43, TITLE:      Automated Measurements of Key Morphological Features of Human Embryos for IVF
http://arxiv.org/abs/2006.00067
AUTHORS:        Brian D. Leahy ; Won-Dong Jang ; Helen Y. Yang ; Robbert Struyven ; Donglai Wei ; Zhe Sun ; Kylie R. Lee ; Charlotte Royston ; Liz Cam ; Yael Kalma ; Foad Azem ; Dalit Ben-Yosef ; Hanspeter Pfister ; Daniel Needleman
COMMENTS:       to be presented at MICCAI 2020
HIGHLIGHT:      Our approach greatly speeds up the measurement of quantitative, biologically relevant features that may aid in embryo selection.

44, TITLE:      Increasing the robustness of DNNs against image corruptions by playing the Game of Noise
http://arxiv.org/abs/2001.06057
AUTHORS:        Evgenia Rusak ; Lukas Schott ; Roland S. Zimmermann ; Julian Bitterwolf ; Oliver Bringmann ; Matthias Bethge ; Wieland Brendel
COMMENTS:       Oral presentation at the European Conference for Computer Vision (ECCV 2020)
HIGHLIGHT:      Here, we demonstrate that a simple but properly tuned training with additive Gaussian and Speckle noise generalizes surprisingly well to unseen corruptions, easily reaching the previous state of the art on the corruption benchmark ImageNet-C (with ResNet50) and on MNIST-C.

45, TITLE:      Visual Relation Grounding in Videos
http://arxiv.org/abs/2007.08814
AUTHORS:        Junbin Xiao ; Xindi Shang ; Xun Yang ; Sheng Tang ; Tat-Seng Chua
COMMENTS:       ECCV2020 (spotlight)
HIGHLIGHT:      In this paper, we explore a novel task named visual Relation Grounding in Videos (vRGV).

46, 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.

47, TITLE:      A Group-Theoretic Framework for Knowledge Graph Embedding
http://arxiv.org/abs/2005.10956
AUTHORS:        Tong Yang ; Long Sha ; Pengyu Hong
COMMENTS:       8 pages, 3 tables
HIGHLIGHT:      Motivated by the theoretical analysis, we have proposed a group theory-based knowledge graph embedding framework, in which relations are embedded as group elements, and entities are represented by vectors in group action spaces.

48, TITLE:      Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support
http://arxiv.org/abs/2007.09303
AUTHORS:        Xin Deng ; Ross Smith ; Genevieve Quintin
COMMENTS:       7 pages, 7 figures, 2 tables
HIGHLIGHT:      In this paper, we proposed and developed an innovative Semi-Supervised Learning approach by utilizing Deep Learning and Topic Modeling to have a better understanding of the user voice.This approach combines a BERT-based multiclassification algorithm through supervised learning combined with a novel Probabilistic and Semantic Hybrid Topic Inference (PSHTI) Model through unsupervised learning, aiming at automating the process of better identifying the main topics or areas as well as the sub-topics from the textual feedback and support.There are three major break-through: 1.

49, TITLE:      COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
http://arxiv.org/abs/2007.00576
AUTHORS:        Qingyun Wang ; Manling Li ; Xuan Wang ; Nikolaus Parulian ; Guangxing Han ; Jiawei Ma ; Jingxuan Tu ; Ying Lin ; Haoran Zhang ; Weili Liu ; Aabhas Chauhan ; Yingjun Guan ; Bangzheng Li ; Ruisong Li ; Xiangchen Song ; Heng Ji ; Jiawei Han ; Shih-Fu Chang ; James Pustejovsky ; Jasmine Rah ; David Liem ; Ahmed Elsayed ; Martha Palmer ; Clare Voss ; Cynthia Schneider ; Boyan Onyshkevych
COMMENTS:       11 pages, submitted to ACL 2020 Workshop on Natural Language Processing for COVID-19 (NLP-COVID), for resources see http://blender.cs.illinois.edu/covid19/, for video see http://159.89.180.81/demo/covid/Covid-KG_DemoVideo.mp4
HIGHLIGHT:      We have developed a novel and comprehensive knowledge discovery framework, \textbf{COVID-KG} to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature.

50, TITLE:      Controlling Style and Semantics in Weakly-Supervised Image Generation
http://arxiv.org/abs/1912.03161
AUTHORS:        Dario Pavllo ; Aurelien Lucchi ; Thomas Hofmann
COMMENTS:       European Conference on Computer Vision (ECCV) 2020, Spotlight. Code at https://github.com/dariopavllo/style-semantics
HIGHLIGHT:      We propose a weakly-supervised approach for conditional image generation of complex scenes where a user has fine control over objects appearing in the scene.

51, TITLE:      Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
http://arxiv.org/abs/2006.13165
AUTHORS:        Dongruo Zhou ; Jiafan He ; Quanquan Gu
COMMENTS:       28 pages, 1 figure
HIGHLIGHT:      In this paper, we study reinforcement learning with feature mapping for discounted Markov Decision Processes (MDPs).

52, 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 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.

53, TITLE:      Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
http://arxiv.org/abs/2003.09150
AUTHORS:        Fan Zhang ; Meng Li ; Guisheng Zhai ; Yizhao Liu
HIGHLIGHT:      Our approach can be trained end-to-end, while provides short inference time.

54, TITLE:      CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search
http://arxiv.org/abs/2007.09380
AUTHORS:        Xin Chen ; Yawen Duan ; Zewei Chen ; Hang Xu ; Zihao Chen ; Xiaodan Liang ; Tong Zhang ; Zhenguo Li
COMMENTS:       Published at ECCV2020
HIGHLIGHT:      This is the first work to our knowledge that proposes an efficient transferrable NAS solution while maintaining robustness across various settings.

55, TITLE:      Adversarial Continual Learning
http://arxiv.org/abs/2003.09553
AUTHORS:        Sayna Ebrahimi ; Franziska Meier ; Roberto Calandra ; Trevor Darrell ; Marcus Rohrbach
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties.

56, TITLE:      ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling
http://arxiv.org/abs/2004.01893
AUTHORS:        Neeraj Dhanraj Bokde ; Zaher Mundher Yaseen ; Gorm Bruun Andersen
COMMENTS:       Published in Energies
HIGHLIGHT:      This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset.

57, TITLE:      A Separation Logic to Verify Termination of Busy-Waiting for Abrupt Program Exit: Technical Report
http://arxiv.org/abs/2007.10215
AUTHORS:        Tobias Reinhard ; Amin Timany ; Bart Jacobs
COMMENTS:       22 pages, 14 figures, Technical report (replacement: corrected citation in conclusion)
HIGHLIGHT:      In this paper, we make a first step towards proving termination of such programs.

58, TITLE:      Morphological Skip-Gram: Using morphological knowledge to improve word representation
http://arxiv.org/abs/2007.10055
AUTHORS:        Flávio Santos ; Hendrik Macedo ; Thiago Bispo ; Cleber Zanchettin
COMMENTS:       11 pages
HIGHLIGHT:      In this work, we propose a new method for training word embeddings, and its goal is to replace the FastText bag of character n-grams for a bag of word morphemes through the morphological analysis of the word.

59, TITLE:      DeepMSRF: A novel Deep Multimodal Speaker Recognition framework with Feature selection
http://arxiv.org/abs/2007.06809
AUTHORS:        Ehsan Asali ; Farzan Shenavarmasouleh ; Farid Ghareh Mohammadi ; Prasanth Sengadu Suresh ; Hamid R. Arabnia
COMMENTS:       The 24th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'20: July 27-30, 2020, USA)
HIGHLIGHT:      In this paper, we address this problem from a different perspective and propose an unprecedented multimodality data fusion framework called DeepMSRF, Deep Multimodal Speaker Recognition with Feature selection.

60, TITLE:      You Are Here: Geolocation by Embedding Maps and Images
http://arxiv.org/abs/1911.08797
AUTHORS:        Noe Samano ; Mengjie Zhou ; Andrew Calway
COMMENTS:       18 pages, new version accepted for ECCV 2020 (poster), with new results on publicly available dataset and comparison with implementation of previously published alternative approach
HIGHLIGHT:      We present a novel approach to geolocalising panoramic images on a 2-D cartographic map based on learning a low dimensional embedded space, which allows a comparison between an image captured at a location and local neighbourhoods of the map.

61, TITLE:      AutoScale: Learning to Scale for Crowd Counting
http://arxiv.org/abs/1912.09632
AUTHORS:        Chenfeng Xu ; Dingkang Liang ; Yongchao Xu ; Song Bai ; Wei Zhan ; Xiang Bai ; Masayoshi Tomizuka
COMMENTS:       the code is available at https://github.com/dk-liang/AutoScale.git
HIGHLIGHT:      In this paper, we aim to address this long-tailed distribution issue in the density map.

62, TITLE:      Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks
http://arxiv.org/abs/2003.06696
AUTHORS:        Chankyu Lee ; Adarsh Kumar Kosta ; Alex Zihao Zhu ; Kenneth Chaney ; Kostas Daniilidis ; Kaushik Roy
HIGHLIGHT:      To overcome these issues, we present Spike-FlowNet, a deep hybrid neural network architecture integrating SNNs and ANNs for efficiently estimating optical flow from sparse event camera outputs without sacrificing the performance.

63, TITLE:      General 3D Room Layout from a Single View by Render-and-Compare
http://arxiv.org/abs/2001.02149
AUTHORS:        Sinisa Stekovic ; Shreyas Hampali ; Mahdi Rad ; Sayan Deb Sarkar ; Friedrich Fraundorfer ; Vincent Lepetit
HIGHLIGHT:      We present a novel method to reconstruct the 3D layout of a room (walls, floors, ceilings) from a single perspective view in challenging conditions, by contrast with previous single-view methods restricted to cuboid-shaped layouts.

64, TITLE:      Feature Fusion for Online Mutual Knowledge Distillation
http://arxiv.org/abs/1904.09058
AUTHORS:        Jangho Kim ; Minsung Hyun ; Inseop Chung ; Nojun Kwak
COMMENTS:       International Conference on Pattern Recognition
HIGHLIGHT:      We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks.

65, TITLE:      Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer
http://arxiv.org/abs/2006.15264
AUTHORS:        Hajar Emami ; Ming Dong ; Carri K. Glide-Hurst
COMMENTS:       IEEE 21st International Conference on Information Reuse and Integration for Data Science
HIGHLIGHT:      In this paper, we propose a novel spatial attention-guided generative adversarial network (attention-GAN) model to generate accurate synCTs using T1-weighted MRI images as the input to address atypical anatomy.

66, TITLE:      Learning from Noisy Labels with Deep Neural Networks: A Survey
http://arxiv.org/abs/2007.08199
AUTHORS:        Hwanjun Song ; Minseok Kim ; Dongmin Park ; Jae-Gil Lee
COMMENTS:       If your paper is highly related, but it is missing, please contact me: songhwanjun@kaist.ac.kr
HIGHLIGHT:      Subsequently, we summarize the typically used evaluation methodology, including public noisy datasets and evaluation metrics.

67, TITLE:      How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance
http://arxiv.org/abs/2002.09040
AUTHORS:        Miqing Li ; Tao Chen ; Xin Yao
COMMENTS:       under the second round of review, 9 figures and 9 tables
HIGHLIGHT:      In this paper, we first carry out a systematic and critical review of quality evaluation for multi-objective optimization in SBSE.