2020.03.27.txt

==========New Papers==========
1, TITLE:       Memory Enhanced Global-Local Aggregation for Video Object Detection
http://arxiv.org/abs/2003.12063
AUTHORS:        Yihong Chen ; Yue Cao ; Han Hu ; Liwei Wang
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this paper we introduce memory enhanced global-local aggregation (MEGA) network, which is among the first trials that takes full consideration of both global and local information.

2, TITLE:       Accelerated Analog Neuromorphic Computing
http://arxiv.org/abs/2003.11996
AUTHORS:        Johannes Schemmel ; Sebastian Billaudelle ; Phillip Dauer ; Johannes Weis
HIGHLIGHT:      This paper presents the concepts behind the BrainScales (BSS) accelerated analog neuromorphic computing architecture.

3, TITLE:       Weakly-supervised 3D coronary artery reconstruction from two-view angiographic images
http://arxiv.org/abs/2003.11846
AUTHORS:        Lu Wang ; Dong-xue Liang ; Xiao-lei Yin ; Jing Qiu ; Zhi-yun Yang ; Jun-hui Xing ; Jian-zeng Dong ; Zhao-yuan Ma
HIGHLIGHT:      We propose an adversarial and generative way to reconstruct three dimensional coronary artery models, from two different views of angiographic images of coronary arteries.

4, TITLE:       Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net
http://arxiv.org/abs/2003.11851
AUTHORS:        Lu Wang ; Dong-xue Liang ; Xiao-lei Yin ; Jing Qiu ; Zhi-yun Yang ; Jun-hui Xing ; Jian-zeng Dong ; Zhao-yuan Ma
HIGHLIGHT:      This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels.

5, TITLE:       Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
http://arxiv.org/abs/2003.11617
AUTHORS:        Ioannis D. Apostolopoulos ; Tzani Bessiana
HIGHLIGHT:      In this study, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus.

6, TITLE:       COVID-19 Image Data Collection
http://arxiv.org/abs/2003.11597
AUTHORS:        Joseph Paul Cohen ; Paul Morrison ; Lan Dao
COMMENTS:       Dataset available here: https://github.com/ieee8023/covid-chestxray-dataset
HIGHLIGHT:      This paper describes the initial COVID-19 open image data collection.

7, TITLE:       Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images
http://arxiv.org/abs/2003.11988
AUTHORS:        Zhenyu Tang ; Wei Zhao ; Xingzhi Xie ; Zheng Zhong ; Feng Shi ; Jun Liu ; Dinggang Shen
HIGHLIGHT:      A random forest (RF) model is trained to assess the severity (non-severe or severe) based on quantitative features.

8, TITLE:       Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting
http://arxiv.org/abs/2003.12025
AUTHORS:        Saeed Khaki ; Hieu Pham ; Ye Han ; Andy Kuhl ; Wade Kent ; Lizhi Wang
COMMENTS:       8 pages, 10 figures
HIGHLIGHT:      In this paper, we propose a kernel detection and counting method based on a sliding window approach.

9, TITLE:       Milking CowMask for Semi-Supervised Image Classification
http://arxiv.org/abs/2003.12022
AUTHORS:        Geoff French ; Avital Oliver ; Tim Salimans
COMMENTS:       10 pages, 3 figured, submitted to ICML 2020
HIGHLIGHT:      Here, we evaluate the use of a recently proposed augmentation method, called CowMasK, for this purpose.

10, TITLE:      Towards Backward-Compatible Representation Learning
http://arxiv.org/abs/2003.11942
AUTHORS:        Yantao Shen ; Yuanjun Xiong ; Wei Xia ; Stefano Soatto
COMMENTS:       Accepted to CVPR 2020 as oral
HIGHLIGHT:      We propose a framework to train embedding models, called backward-compatible training (BCT), as a first step towards backward compatible representation learning.

11, TITLE:      RAFT: Recurrent All-Pairs Field Transforms for Optical Flow
http://arxiv.org/abs/2003.12039
AUTHORS:        Zachary Teed ; Jia Deng
HIGHLIGHT:      We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for optical flow.

12, TITLE:      R-FORCE: Robust Learning for Random Recurrent Neural Networks
http://arxiv.org/abs/2003.11660
AUTHORS:        Yang Zheng ; Eli Shlizerman
COMMENTS:       Github Repository: https://github.com/shlizee/R-FORCE
HIGHLIGHT:      Specifically, FORCE learning approach proposed a recursive least squares alternative to train RRNN and was shown to be applicable even for the challenging task of target-learning, where the network is tasked with generating dynamic patterns with no guiding input.

13, TITLE:      Learning to Correct Overexposed and Underexposed Photos
http://arxiv.org/abs/2003.11596
AUTHORS:        Mahmoud Afifi ; Konstantinos G. Derpanis ; Björn Ommer ; Michael S. Brown
HIGHLIGHT:      Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each sub-problem separately.

14, TITLE:      Fastidious Attention Network for Navel Orange Segmentation
http://arxiv.org/abs/2003.11734
AUTHORS:        Xiaoye Sun ; Gongyan Li ; Shaoyun Xu
HIGHLIGHT:      Deep learning achieves excellent performance in many domains, so we not only apply it to the navel orange semantic segmentation task to solve the two problems of distinguishing defect categories and identifying the stem end and blossom end, but also propose a fastidious attention mechanism to further improve model performance.

15, TITLE:      Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models
http://arxiv.org/abs/2003.11743
AUTHORS:        Pranav Agarwal ; Alejandro Betancourt ; Vana Panagiotou ; Natalia Díaz-Rodríguez
COMMENTS:       15 pages, 25 figures
HIGHLIGHT:      In this paper, we attempt to show the biased nature of the currently existing image captioning models and present a new image captioning dataset, Egoshots, consisting of 978 real life images with no captions.

16, TITLE:      Mask Encoding for Single Shot Instance Segmentation
http://arxiv.org/abs/2003.11712
AUTHORS:        Rufeng Zhang ; Zhi Tian ; Chunhua Shen ; Mingyu You ; Youliang Yan
COMMENTS:       Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020
HIGHLIGHT:      In this work, we propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst).

17, TITLE:      Too many cooks: Coordinating multi-agent collaboration through inverse planning
http://arxiv.org/abs/2003.11778
AUTHORS:        Rose E. Wang ; Sarah A. Wu ; James A. Evans ; Joshua B. Tenenbaum ; David C. Parkes ; Max Kleiman-Weiner
COMMENTS:       Rose E. Wang and Sarah A. Wu contributed equally. Accepted as an extended abstract to AAMAS 2020
HIGHLIGHT:      Here, we develop Bayesian Delegation, a decentralized multi-agent learning mechanism with these abilities.

18, TITLE:      TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance Generation
http://arxiv.org/abs/2003.11963
AUTHORS:        Shaojie Jiang ; Thomas Wolf ; Christof Monz ; Maarten de Rijke
COMMENTS:       9 pages, 4 figures, 1 table
HIGHLIGHT:      In this work, we study the repetition problem for encoder-decoder models, using both recurrent neural network (RNN) and transformer architectures.

19, TITLE:      A Critique on the Interventional Detection of Causal Relationships
http://arxiv.org/abs/2003.11706
AUTHORS:        Mehrzad Saremi
COMMENTS:       15 pages, 6 figures
HIGHLIGHT:      In this paper, we will inspect how interventions influence the interpretation of causation in causal models in specific situation.

20, TITLE:      Choice functions based on sets of strict partial orders: an axiomatic characterisation
http://arxiv.org/abs/2003.11631
AUTHORS:        Jasper De Bock
HIGHLIGHT:      I here provide a very general axiomatic characterisation for choice functions of this form.

21, TITLE:      Deep Networks as Logical Circuits: Generalization and Interpretation
http://arxiv.org/abs/2003.11619
AUTHORS:        Christopher Snyder ; Sriram Vishwanath
HIGHLIGHT:      We present a hierarchical decomposition of the DNN discrete classification map into logical (AND/OR) combinations of intermediate (True/False) classifiers of the input.

22, TITLE:      Interval Neural Networks: Uncertainty Scores
http://arxiv.org/abs/2003.11566
AUTHORS:        Luis Oala ; Cosmas Heiß ; Jan Macdonald ; Maximilian März ; Wojciech Samek ; Gitta Kutyniok
COMMENTS:       LO and CH contributed equally
HIGHLIGHT:      We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network.

23, TITLE:      iTAML: An Incremental Task-Agnostic Meta-learning Approach
http://arxiv.org/abs/2003.11652
AUTHORS:        Jathushan Rajasegaran ; Salman Khan ; Munawar Hayat ; Fahad Shahbaz Khan ; Mubarak Shah
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks.

24, TITLE:      Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images
http://arxiv.org/abs/2003.12040
AUTHORS:        Qilei Chen ; Ping Liu ; Jing Ni ; Yu Cao ; Benyuan Liu ; Honggang Zhang
HIGHLIGHT:      In this work, we investigate lesion detection on DR fundus images with CNN-based object detection methods.

25, TITLE:      Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects
http://arxiv.org/abs/2003.12045
AUTHORS:        Kiana Ehsani ; Shubham Tulsiani ; Saurabh Gupta ; Ali Farhadi ; Abhinav Gupta
COMMENTS:       CVPR 2020 -- (Oral presentation)
HIGHLIGHT:      In this paper, we take a step towards a more physical understanding of actions.

26, TITLE:      Grounded Situation Recognition
http://arxiv.org/abs/2003.12058
AUTHORS:        Sarah Pratt ; Mark Yatskar ; Luca Weihs ; Ali Farhadi ; Aniruddha Kembhavi
HIGHLIGHT:      We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images describing: the primary activity, entities engaged in the activity with their roles (e.g. agent, tool), and bounding-box groundings of entities.

27, TITLE:      Learning Inverse Rendering of Faces from Real-world Videos
http://arxiv.org/abs/2003.12047
AUTHORS:        Yuda Qiu ; Zhangyang Xiong ; Kai Han ; Zhongyuan Wang ; Zixiang Xiong ; Xiaoguang Han
COMMENTS:       First two authors contributed equally. Code:https://github.com/RudyQ/InverseFaceRender
HIGHLIGHT:      In this paper we examine the problem of inverse rendering of real face images.

28, TITLE:      Rethinking Online Action Detection in Untrimmed Videos: A Novel Online Evaluation Protocol
http://arxiv.org/abs/2003.12041
AUTHORS:        Marcos Baptista Rios ; Roberto J. López-Sastre ; Fabian Caba Heilbron ; Jan van Gemert ; F. Javier Acevedo-Rodríguez ; S. Maldonado-Bascón
COMMENTS:       Published at IEEE Access journal
HIGHLIGHT:      In this work we propose to rethink the OAD scenario, clearly defining the problem itself and the main characteristics that the models which are considered online must comply with.

29, TITLE:      Correspondence Networks with Adaptive Neighbourhood Consensus
http://arxiv.org/abs/2003.12059
AUTHORS:        Shuda Li ; Kai Han ; Theo W. Costain ; Henry Howard-Jenkins ; Victor Prisacariu
COMMENTS:       CVPR 2020. Project page: https://ancnet.avlcode.org/
HIGHLIGHT:      In this paper, we tackle the task of establishing dense visual correspondences between images containing objects of the same category.

30, TITLE:      Are Labels Necessary for Neural Architecture Search?
http://arxiv.org/abs/2003.12056
AUTHORS:        Chenxi Liu ; Piotr Dollár ; Kaiming He ; Ross Girshick ; Alan Yuille ; Saining Xie
HIGHLIGHT:      In this paper, we ask the question: can we find high-quality neural architectures using only images, but no human-annotated labels?

31, TITLE:      Negative Margin Matters: Understanding Margin in Few-shot Classification
http://arxiv.org/abs/2003.12060
AUTHORS:        Bin Liu ; Yue Cao ; Yutong Lin ; Qi Li ; Zheng Zhang ; Mingsheng Long ; Han Hu
COMMENTS:       Code is available at https://github.com/bl0/negative-margin.few-shot
HIGHLIGHT:      This paper introduces a negative margin loss to metric learning based few-shot learning methods.

32, TITLE:      Spike-Timing-Dependent Back Propagation in Deep Spiking Neural Networks
http://arxiv.org/abs/2003.11837
AUTHORS:        Malu Zhang ; Jiadong Wang ; Zhixuan Zhang ; Ammar Belatreche ; Jibin Wu ; Yansong Chua ; Hong Qu ; Haizhou Li
HIGHLIGHT:      To address this problem, we propose a simple yet efficient Rectified Linear Postsynaptic Potential function (ReL-PSP) for spiking neurons and propose a Spike-Timing-Dependent Back-Propagation (STDBP) learning algorithm for DSNNs.

33, TITLE:      T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
http://arxiv.org/abs/2003.11741
AUTHORS:        Seongsik Park ; Seijoon Kim ; Byunggook Na ; Sungroh Yoon
COMMENTS:       Accepted to DAC 2020
HIGHLIGHT:      In this paper, we present T2FSNN, which introduces the concept of time-to-first-spike coding into deep SNNs using the kernel-based dynamic threshold and dendrite to overcome the aforementioned drawback.

34, TITLE:      DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving Systems
http://arxiv.org/abs/2003.11766
AUTHORS:        Sai Krishna Bashetty ; Heni Ben Amor ; Georgios Fainekos
COMMENTS:       8 pages, 5 figures, ICRA 2020, Trajectory Extraction, Trajectory Simulation
HIGHLIGHT:      The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles.

35, TITLE:      Multi-User Remote lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm
http://arxiv.org/abs/2003.11708
AUTHORS:        Seid Miad Zandavi ; Vera Chung ; Ali Anaissi
HIGHLIGHT:      The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm and Non-dominated Sorting Genetic Algorithm (NSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access.

36, TITLE:      Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
http://arxiv.org/abs/2003.11593
AUTHORS:        Hamid Jalalzai ; Pierre Colombo ; Chloé Clavel ; Eric Gaussier ; Giovanna Varni ; Emmanuel Vignon ; Anne Sabourin
HIGHLIGHT:      In this paper, we develop a novel method to learn a heavy-tailed embedding with desirable regularity properties regarding the distributional tails, which allows to analyze the points far away from the distribution bulk using the framework of multivariate extreme value theory.

37, TITLE:      A Topological Characterization of Modulo-p Arguments and Implications for Necklace Splitting
http://arxiv.org/abs/2003.11974
AUTHORS:        Aris Filos-Ratsikas ; Alexandros Hollender ; Katerina Sotiraki ; Manolis Zampetakis
HIGHLIGHT:      In this paper, we provide the first topological characterization of the classes PPA-$p$.

38, TITLE:      Common-Knowledge Concept Recognition for SEVA
http://arxiv.org/abs/2003.11687
AUTHORS:        Jitin Krishnan ; Patrick Coronado ; Hemant Purohit ; Huzefa Rangwala
COMMENTS:       Source code available
HIGHLIGHT:      We use a pre-trained language model and fine-tune it with the labeled dataset of concepts. With the help of a domain expert and text processing methods, we construct a dataset annotated at the word-level by carefully defining a labelling scheme to train a sequence model to recognize systems engineering concepts. In addition, we also create some essential datasets for information such as abbreviations and definitions from the systems engineering domain.

39, TITLE:      No-Rainbow Problem is NP-Hard
http://arxiv.org/abs/2003.11764
AUTHORS:        Dmitriy Zhuk
HIGHLIGHT:      In this paper we show that one of the most popular variants of the SCSP, called No-Rainbow Problem, is NP-Hard.

40, TITLE:      BachGAN: High-Resolution Image Synthesis from Salient Object Layout
http://arxiv.org/abs/2003.11690
AUTHORS:        Yandong Li ; Yu Cheng ; Zhe Gan ; Licheng Yu ; Liqiang Wang ; Jingjing Liu
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We propose a new task towards more practical application for image generation - high-quality image synthesis from salient object layout.

41, TITLE:      VIOLIN: A Large-Scale Dataset for Video-and-Language Inference
http://arxiv.org/abs/2003.11618
AUTHORS:        Jingzhou Liu ; Wenhu Chen ; Yu Cheng ; Zhe Gan ; Licheng Yu ; Yiming Yang ; Jingjing Liu
COMMENTS:       Accepted to CVPR2020
HIGHLIGHT:      We introduce a new task, Video-and-Language Inference, for joint multimodal understanding of video and text.

42, TITLE:      Classification of the Chinese Handwritten Numbers with Supervised Projective Dictionary Pair Learning
http://arxiv.org/abs/2003.11700
AUTHORS:        Rasool Ameri ; Saideh Ferdowsi ; Ali Alameer ; Vahid Abolghasemi ; Kianoush Nazarpour
HIGHLIGHT:      To mitigate this problem, in this paper a novel dictionary learning method is proposed and tested with Chinese handwritten numbers.

43, TITLE:      Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis
http://arxiv.org/abs/2003.11571
AUTHORS:        Wei Sun ; Tianfu Wu
HIGHLIGHT:      With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable inputs.

44, TITLE:      Deep Grouping Model for Unified Perceptual Parsing
http://arxiv.org/abs/2003.11647
AUTHORS:        Zhiheng Li ; Wenxuan Bao ; Jiayang Zheng ; Chenliang Xu
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      Overcoming these challenges, we propose a deep grouping model (DGM) that tightly marries the two types of representations and defines a bottom-up and a top-down process for feature exchanging.

45, TITLE:      DeepStrip: High Resolution Boundary Refinement
http://arxiv.org/abs/2003.11670
AUTHORS:        Peng Zhou ; Brian Price ; Scott Cohen ; Gregg Wilensky ; Larry S. Davis
HIGHLIGHT:      In this paper, we target refining the boundaries in high resolution images given low resolution masks.

46, TITLE:      Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features
http://arxiv.org/abs/2003.11797
AUTHORS:        Kai Qiao ; Chi Zhang ; Jian Chen ; Linyuan Wang ; Li Tong ; Bin Yan
HIGHLIGHT:      In this study, we introduced one higher-level vision task: image caption (IC) task and proposed the visual encoding model based on IC features (ICFVEM) to encode voxels of high-level visual cortices.

47, TITLE:      Compact Deep Aggregation for Set Retrieval
http://arxiv.org/abs/2003.11794
AUTHORS:        Yujie Zhong ; Relja Arandjelović ; Andrew Zisserman
COMMENTS:       20 pages
HIGHLIGHT:      The objective of this work is to learn a compact embedding of a set of descriptors that is suitable for efficient retrieval and ranking, whilst maintaining discriminability of the individual descriptors. Here the set consists of the face descriptors in each image, and given a query for multiple identities, the goal is then to retrieve, in order, images which contain all the identities, all but one, \etc To this end, we make the following contributions: first, we propose a CNN architecture -- {\em SetNet} -- to achieve the objective: it learns face descriptors and their aggregation over a set to produce a compact fixed length descriptor designed for set retrieval, and the score of an image is a count of the number of identities that match the query; second, we show that this compact descriptor has minimal loss of discriminability up to two faces per image, and degrades slowly after that -- far exceeding a number of baselines; third, we explore the speed vs.\ retrieval quality trade-off for set retrieval using this compact descriptor; and, finally, we collect and annotate a large dataset of images containing various number of celebrities, which we use for evaluation and is publicly released.

48, TITLE:      Do Deep Minds Think Alike? Selective Adversarial Attacks for Fine-Grained Manipulation of Multiple Deep Neural Networks
http://arxiv.org/abs/2003.11816
AUTHORS:        Zain Khan ; Jirong Yi ; Raghu Mudumbai ; Xiaodong Wu ; Weiyu Xu
COMMENTS:       9 pages, submitted to ICML 2020
HIGHLIGHT:      In this paper we ask a simple but fundamental question of "selective fooling": given {\it multiple} machine learning systems assigned to solve the same classification problem and taking the same input signal, is it possible to construct a perturbation to the input signal that manipulates the outputs of these {\it multiple} machine learning systems {\it simultaneously} in arbitrary pre-defined ways?

49, TITLE:      Real-time 3D Deep Multi-Camera Tracking
http://arxiv.org/abs/2003.11753
AUTHORS:        Quanzeng You ; Hao Jiang
COMMENTS:       17 pages, 8 figures
HIGHLIGHT:      In this work, we propose a novel end-to-end tracking pipeline, Deep Multi-Camera Tracking (DMCT), which achieves reliable real-time multi-camera people tracking. Apart from evaluation on the challenging WILDTRACK dataset, we also collect two more tracking datasets with high-quality labels from two different environments and camera settings.

50, TITLE:      Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space
http://arxiv.org/abs/2003.11774
AUTHORS:        Khoa D. Doan ; Saurav Manchanda ; Fengjiao Wang ; Sathiya Keerthi ; Avradeep Bhowmik ; Chandan K. Reddy
HIGHLIGHT:      We propose an efficient, numerically stable approach to calculate the Fr\'{e}chet distance and its gradient.

51, TITLE:      The 1st Challenge on Remote Physiological Signal Sensing (RePSS)
http://arxiv.org/abs/2003.11756
AUTHORS:        Xiaobai Li ; Hu Han ; Hao Lu ; Xuesong Niu ; Zitong Yu ; Antitza Dantcheva ; Guoying Zhao ; Shiguang Shan
HIGHLIGHT:      This paper presents an overview of the challenge, including data, protocol, analysis of results and discussion.

52, TITLE:      Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis
http://arxiv.org/abs/2003.11842
AUTHORS:        James Houston ; Frank G. Glavin ; Michael G. Madden
COMMENTS:       Journal of Chemical Information and Modeling
HIGHLIGHT:      This paper presents a new approach to classification of high dimensional spectroscopy data and demonstrates that it outperforms other current state-of-the art approaches.

53, TITLE:      DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation
http://arxiv.org/abs/2003.11883
AUTHORS:        Xiong Zhang ; Hongmin Xu ; Hong Mo ; Jianchao Tan ; Cheng Yang ; Wenqi Ren
HIGHLIGHT:      To allow as wide as possible network architectures and avoid the gap between target and proxy dataset, we propose a Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset.

54, TITLE:      Instance Credibility Inference for Few-Shot Learning
http://arxiv.org/abs/2003.11853
AUTHORS:        Yikai Wang ; Chengming Xu ; Chen Liu ; Li Zhang ; Yanwei Fu
COMMENTS:       accepted by CVPR 2020
HIGHLIGHT:      In contrast, this paper presents a simple statistical approach, dubbed Instance Credibility Inference (ICI) to exploit the distribution support of unlabeled instances for few-shot learning.

55, TITLE:      P $\approx$ NP, at least in Visual Question Answering
http://arxiv.org/abs/2003.11844
AUTHORS:        Shailza Jolly ; Sebastian Palacio ; Joachim Folz ; Federico Raue ; Jorn Hees ; Andreas Dengel
HIGHLIGHT:      In this paper, we measure the potential confounding factors when polar and non-polar samples are used jointly to train a baseline VQA classifier, and compare it to an upper bound where the over-representation of polar questions is excluded from the training.

56, TITLE:      Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
http://arxiv.org/abs/2003.11818
AUTHORS:        Jianyuan Guo ; Kai Han ; Yunhe Wang ; Chao Zhang ; Zhaohui Yang ; Han Wu ; Xinghao Chen ; Chang Xu
COMMENTS:       Accepted in CVPR 2020
HIGHLIGHT:      To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i.e. backbone, neck, and head) of object detector in an end-to-end manner.

57, TITLE:      Zero-Assignment Constraint for Graph Matching with Outliers
http://arxiv.org/abs/2003.11928
AUTHORS:        Fudong Wang ; Nan Xue ; Jin-Gang Yu ; Gui-Song Xia
HIGHLIGHT:      To address this issue, we present the zero-assignment constraint (ZAC) for approaching the graph matching problem in the presence of outliers.

58, TITLE:      Matrix Smoothing: A Regularization for DNN with Transition Matrix under Noisy Labels
http://arxiv.org/abs/2003.11904
AUTHORS:        Xianbin Lv ; Dongxian Wu ; Shu-Tao Xia
COMMENTS:       ICME 2020
HIGHLIGHT:      In this paper, inspired by label smoothing, we proposed a novel method, in which a smoothed transition matrix is used for updating DNN, to restrict the overfitting of DNN in probabilistic modeling.



==========Updates to Previous Papers==========
1, TITLE:       Assignment and Pricing of Shared Rides in Ride-Sourcing using Combinatorial Double Auctions
http://arxiv.org/abs/1909.08608
AUTHORS:        Renos Karamanis ; Eleftherios Anastasiadis ; Panagiotis Angeloudis ; Marc Stettler
COMMENTS:       12 pages, Ride-Sharing, Combinatorial Double Auctions, Assignment, Dynamic Pricing
HIGHLIGHT:      To resolve this, we formulate a new shared-ride assignment and pricing algorithm using combinatorial double auctions.

2, TITLE:       A Nonexistence Certificate for Projective Planes of Order Ten with Weight 15 Codewords
http://arxiv.org/abs/1911.04032
AUTHORS:        Curtis Bright ; Kevin Cheung ; Brett Stevens ; Dominique Roy ; Ilias Kotsireas ; Vijay Ganesh
COMMENTS:       To appear in Applicable Algebra in Engineering, Communication and Computing
HIGHLIGHT:      Using techniques from the fields of symbolic computation and satisfiability checking we verify one of the cases used in the landmark result that projective planes of order ten do not exist.

3, TITLE:       Nonexistence Certificates for Ovals in a Projective Plane of Order Ten
http://arxiv.org/abs/2001.11974
AUTHORS:        Curtis Bright ; Kevin K. H. Cheung ; Brett Stevens ; Ilias Kotsireas ; Vijay Ganesh
COMMENTS:       To appear in Proceedings of the 31st International Workshop on Combinatorial Algorithms (IWOCA 2020)
HIGHLIGHT:      In this paper, we rerun the search for ovals in a projective plane of order ten and produce a collection of nonexistence certificates that, when taken together, imply that such ovals do not exist.

4, TITLE:       PanNuke Dataset Extension, Insights and Baselines
http://arxiv.org/abs/2003.10778
AUTHORS:        Jevgenij Gamper ; Navid Alemi Koohbanani ; Simon Graham ; Mostafa Jahanifar ; Syed Ali Khurram ; Ayesha Azam ; Katherine Hewitt ; Nasir Rajpoot
COMMENTS:       Work in progress
HIGHLIGHT:      We study the performance of segmentation and classification models when applied to the proposed dataset and demonstrate the application of models trained on PanNuke to whole-slide images.

5, TITLE:       Oral-3D: Reconstructing the 3D Bone Structure of Oral Cavity from 2D Panoramic X-ray
http://arxiv.org/abs/2003.08413
AUTHORS:        Weinan Song ; Yuan Liang ; Kun Wang ; Lei He
HIGHLIGHT:      In this paper, we present \textit{Oral-3D} to reconstruct the bone structure of oral cavity from a single panoramic X-ray image by taking advantage of some prior knowledge in oral structure, which conventionally can only be obtained by a 3D imaging method like CBCT.

6, TITLE:       Fast and Automatic Periacetabular Osteotomy Fragment Pose Estimation Using Intraoperatively Implanted Fiducials and Single-View Fluoroscopy
http://arxiv.org/abs/1910.10187
AUTHORS:        Robert Grupp ; Ryan Murphy ; Rachel Hegeman ; Clayton Alexander ; Mathias Unberath ; Yoshito Otake ; Benjamin McArthur ; Mehran Armand ; Russell Taylor
COMMENTS:       Revised article to address reviewer comments. Under review for Physics in Medicine and Biology. Supplementary video at https://youtu.be/0E0U9G81q8g
HIGHLIGHT:      We propose a computer assisted approach that uses a single fluoroscopic view and quickly reports the pose of an acetabular fragment without any user input or initialization.

7, TITLE:       360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
http://arxiv.org/abs/1911.04460
AUTHORS:        Ning-Hsu Wang ; Bolivar Solarte ; Yi-Hsuan Tsai ; Wei-Chen Chiu ; Min Sun
COMMENTS:       Accepted to 2020 IEEE International Conference on Robotics and Automation (ICRA 2020). Project page and code are at https://albert100121.github.io/360SD-Net-Project-Page
HIGHLIGHT:      To tackle this issue, we present a novel architecture specifically designed for spherical disparity using the setting of top-bottom 360{\deg} camera pairs. Due to the lack of 360{\deg} stereo data, we collect two 360{\deg} stereo datasets from Matterport3D and Stanford3D for training and evaluation.

8, TITLE:       GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction
http://arxiv.org/abs/2003.07167
AUTHORS:        Chengxin Wang ; Shaofeng Cai ; Gary Tan
COMMENTS:       16 pages, 5 figures, 2 tables
HIGHLIGHT:      To support a more efficient and accurate trajectory prediction, we instead propose a novel CNN-based spatial-temporal graph framework GraphTCN, which captures the spatial and temporal interactions in an input-aware manner.

9, TITLE:       Visual Grounding in Video for Unsupervised Word Translation
http://arxiv.org/abs/2003.05078
AUTHORS:        Gunnar A. Sigurdsson ; Jean-Baptiste Alayrac ; Aida Nematzadeh ; Lucas Smaira ; Mateusz Malinowski ; João Carreira ; Phil Blunsom ; Andrew Zisserman
COMMENTS:       CVPR 2020
HIGHLIGHT:      Our goal is to use visual grounding to improve unsupervised word mapping between languages.

10, TITLE:      Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning
http://arxiv.org/abs/2003.09391
AUTHORS:        Lei Tian ; Yongqiang Tang ; Liangchen Hu ; Zhida Ren ; Wensheng Zhang
HIGHLIGHT:      Different from existing methods that make label prediction for target samples independently, in this paper, we propose a novel domain adaptation approach that assigns pseudo-labels to target data with the guidance of class centroids in two domains, so that the data distribution structure of both source and target domains can be emphasized.

11, TITLE:      PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting
http://arxiv.org/abs/2001.05643
AUTHORS:        Saeed Amirgholipour ; Xiangjian He ; Wenjing Jia ; Dadong Wang ; Lei Liu
HIGHLIGHT:      In this paper, we propose a novel Pyramid Density-Aware Attention-based network, abbreviated as PDANet, that leverages the attention, pyramid scale feature and two branch decoder modules for density-aware crowd counting.

12, TITLE:      Prior-enlightened and Motion-robust Video Deblurring
http://arxiv.org/abs/2003.11209
AUTHORS:        Ya Zhou ; Jianfeng Xu ; Kazuyuki Tasaka ; Zhibo Chen ; Weiping Li
COMMENTS:       26 pages, 13 figures, and 7 tables
HIGHLIGHT:      Therefore, we propose a PRiOr-enlightened and MOTION-robust video deblurring model (PROMOTION) suitable for challenging blurs.

13, TITLE:      StegaStamp: Invisible Hyperlinks in Physical Photographs
http://arxiv.org/abs/1904.05343
AUTHORS:        Matthew Tancik ; Ben Mildenhall ; Ren Ng
COMMENTS:       CVPR 2020, Project page: http://www.matthewtancik.com/stegastamp
HIGHLIGHT:      This paper presents an architecture, algorithms, and a prototype implementation addressing this vision.

14, TITLE:      Parallel Medical Imaging: A New Data-Knowledge-Driven Evolutionary Framework for Medical Image Analysis
http://arxiv.org/abs/1903.04855
AUTHORS:        Chao Gou ; Tianyu Shen ; Wenbo Zheng ; Oliver Kwan ; Fei-Yue Wang
COMMENTS:       We would like to withdraw this manuscript since it lacks comprehensive experiments to validate the main points. In addition, it is too wordy and it may confuse the readers in some parts. We will revise it thoroughly in future work
HIGHLIGHT:      In this paper, we propose a data-knowledge-driven evolutionary framework termed as Parallel Medical Imaging (PMI) for medical image analysis based on the methodology of interactive ACP-based parallel intelligence.

15, TITLE:      Sparse Black-box Video Attack with Reinforcement Learning
http://arxiv.org/abs/2001.03754
AUTHORS:        Huanqian Yan ; Xingxing Wei ; Bo Li
HIGHLIGHT:      Instead, we argue the frame selection phase is closely relevant with the attacking phase.

16, TITLE:      Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
http://arxiv.org/abs/1901.08460
AUTHORS:        Valentina Zantedeschi ; Aurélien Bellet ; Marc Tommasi
COMMENTS:       To appear in the proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)
HIGHLIGHT:      We propose to train personalized models that leverage a collaboration graph describing the relationships between user personal tasks, which we learn jointly with the models.

17, TITLE:      Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation
http://arxiv.org/abs/1912.00320
AUTHORS:        Wen Zhang ; Dongrui Wu
COMMENTS:       Int'l Joint Conf. on Neural Networks (IJCNN), Glasgow, UK, July 2020
HIGHLIGHT:      To address these issues, discriminative joint probability MMD (DJP-MMD) is proposed in this paper to replace the frequently-used joint MMD in domain adaptation.

18, TITLE:      Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction
http://arxiv.org/abs/2003.11518
AUTHORS:        Yan Xiao ; Yaochu Jin ; Ran Cheng ; Kuangrong Hao
HIGHLIGHT:      To address this issue, we propose a new framework using hybrid attention-based Transformer block with multi-instance learning to perform the DSRE task.

19, TITLE:      Towards Making the Most of BERT in Neural Machine Translation
http://arxiv.org/abs/1908.05672
AUTHORS:        Jiacheng Yang ; Mingxuan Wang ; Hao Zhou ; Chengqi Zhao ; Yong Yu ; Weinan Zhang ; Lei Li
HIGHLIGHT:      In this work, we introduce a concerted training framework (\method) that is the key to integrate the pre-trained LMs to neural machine translation (NMT).

20, TITLE:      Human Action Performance using Deep Neuro-Fuzzy Recurrent Attention Model
http://arxiv.org/abs/2001.10953
AUTHORS:        Nihar Bendre ; Nima Ebadi ; John J Prevost ; Paul Rad
COMMENTS:       1 pages, 6 figures, 2 algorithms. Published at IEEE Access
HIGHLIGHT:      To remedy this uncertainty, in this paper we coupled fuzzy logic rules with the neural-based action recognition model to rate the intensity of a human action as intense or mild.

21, TITLE:      Archimedean Choice Functions: an Axiomatic Foundation for Imprecise Decision Making
http://arxiv.org/abs/2002.05196
AUTHORS:        Jasper De Bock
HIGHLIGHT:      If an imprecise probability model is used instead, this decision rule can be generalised in several ways. For each of these two decision rules, we provide a set of necessary and sufficient conditions on choice functions that uniquely characterises this rule, thereby providing an axiomatic foundation for imprecise decision making with sets of probabilities.

22, TITLE:      A Journey into Ontology Approximation: From Non-Horn to Horn
http://arxiv.org/abs/2001.07754
AUTHORS:        Anneke Haga ; Carsten Lutz ; Johannes Marti ; Frank Wolter
COMMENTS:       20 pages, 4 figures
HIGHLIGHT:      We study complete approximations of an ontology formulated in a non-Horn description logic (DL) such as $\mathcal{ALC}$ in a Horn DL such as~$\mathcal{EL}$.

23, TITLE:      High-level signatures and initial semantics
http://arxiv.org/abs/1805.03740
AUTHORS:        Benedikt Ahrens ; André Hirschowitz ; Ambroise Lafont ; Marco Maggesi
COMMENTS:       v2: extended version of the article as published in CSL 2018 (http://dx.doi.org/10.4230/LIPIcs.CSL.2018.4); list of changes given in Section 1.5 of the paper; v3: small corrections throughout the paper, no major changes
HIGHLIGHT:      We present a device for specifying and reasoning about syntax for datatypes, programming languages, and logic calculi.

24, TITLE:      Founded Semantics and Constraint Semantics of Logic Rules
http://arxiv.org/abs/1606.06269
AUTHORS:        Yanhong A. Liu ; Scott D. Stoller
HIGHLIGHT:      This paper describes a simple new semantics for logic rules, founded semantics, and its straightforward extension to another simple new semantics, constraint semantics, that unify the core of different prior semantics.

25, TITLE:      Neural Contours: Learning to Draw Lines from 3D Shapes
http://arxiv.org/abs/2003.10333
AUTHORS:        Difan Liu ; Mohamed Nabail ; Aaron Hertzmann ; Evangelos Kalogerakis
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      This paper introduces a method for learning to generate line drawings from 3D models.

26, TITLE:      Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
http://arxiv.org/abs/1910.13324
AUTHORS:        Yuan Zhou ; Hongseok Yang ; Yee Whye Teh ; Tom Rainforth
HIGHLIGHT:      To address this, we introduce a new inference framework: Divide, Conquer, and Combine, which remains efficient for such models, and show how it can be implemented as an automated and general-purpose PPS inference engine.

27, TITLE:      Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
http://arxiv.org/abs/1905.01969
AUTHORS:        Samuel Humeau ; Kurt Shuster ; Marie-Anne Lachaux ; Jason Weston
COMMENTS:       ICLR 2020
HIGHLIGHT:      In this work, we develop a new transformer architecture, the Poly-encoder, that learns global rather than token level self-attention features.

28, TITLE:      Enhancing Out-Of-Domain Utterance Detection with Data Augmentation Based on Word Embeddings
http://arxiv.org/abs/1911.10439
AUTHORS:        Yueqi Feng ; Jiali Lin
COMMENTS:       I see some improvements that can be done. There will be a major change regarding the main idea
HIGHLIGHT:      In this paper, we study how augmented OOD data based on sampling impact OOD utterance detection with a small sample size.

29, TITLE:      A Label Proportions Estimation Technique for Adversarial Domain Adaptation in Text Classification
http://arxiv.org/abs/2003.07444
AUTHORS:        Zhuohao Chen ; Singla Karan ; David C. Atkins ; Zac E Imel ; Shrikanth Narayanan
COMMENTS:       add a proposition and a proof of it, correct typos
HIGHLIGHT:      In this study, we focus on unsupervised domain adaptation of text classification with label shift and introduce a domain adversarial network with label proportions estimation (DAN-LPE) framework.

30, TITLE:      Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph
http://arxiv.org/abs/2003.08612
AUTHORS:        Chenguang Zhu ; William Hinthorn ; Ruochen Xu ; Qingkai Zeng ; Michael Zeng ; Xuedong Huang ; Meng Jiang
COMMENTS:       14 pages, 2 figures
HIGHLIGHT:      In this paper, we propose to boost factual correctness of summaries via the fusion of knowledge, i.e. extracted factual relations from the article.

31, TITLE:      Vector logic and counterfactuals
http://arxiv.org/abs/2003.11519
AUTHORS:        Eduardo Mizraji
COMMENTS:       This is a 12 pages preprint
HIGHLIGHT:      In this work we investigate the representation of counterfactual conditionals using the vector logic, a matrix-vectors formalism for logical functions and truth values.

32, TITLE:      Bare quantum simultaneity versus classical interactivity in communication complexity
http://arxiv.org/abs/1911.01381
AUTHORS:        Dmitry Gavinsky
HIGHLIGHT:      Bare quantum simultaneity versus classical interactivity in communication complexity

33, TITLE:      Putting visual object recognition in context
http://arxiv.org/abs/1911.07349
AUTHORS:        Mengmi Zhang ; Claire Tseng ; Gabriel Kreiman
COMMENTS:       8 pages, CVPR2020
HIGHLIGHT:      We propose a biologically-inspired context-aware object recognition model consisting of a two-stream architecture.

34, TITLE:      Latency-Aware Differentiable Neural Architecture Search
http://arxiv.org/abs/2001.06392
AUTHORS:        Yuhui Xu ; Lingxi Xie ; Xiaopeng Zhang ; Xin Chen ; Bowen Shi ; Qi Tian ; Hongkai Xiong
COMMENTS:       19 pages, 9 figures
HIGHLIGHT:      We evaluate our approach on NVIDIA Tesla-P100 GPUs.

35, TITLE:      When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
http://arxiv.org/abs/1911.10695
AUTHORS:        Minghao Guo ; Yuzhe Yang ; Rui Xu ; Ziwei Liu ; Dahua Lin
COMMENTS:       CVPR 2020. First two authors contributed equally
HIGHLIGHT:      In this work, we take an architectural perspective and investigate the patterns of network architectures that are resilient to adversarial attacks.

36, TITLE:      Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework
http://arxiv.org/abs/1911.11251
AUTHORS:        Tobias Schlosser ; Michael Friedrich ; Danny Kowerko
COMMENTS:       Accepted for: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)
HIGHLIGHT:      This contribution serves as a general application-oriented approach the synthesis of the therefore designed hexagonal image processing framework, called Hexnet, the processing steps of hexagonal image transformation, and dependent methods.

37, TITLE:      G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features
http://arxiv.org/abs/2003.11089
AUTHORS:        Wei Chen ; Xi Jia ; Hyung Jin Chang ; Jinming Duan ; Ales Leonardis
COMMENTS:       10 pages, 11 figures, accepted in CVPR 2020
HIGHLIGHT:      In this paper, we propose a novel real-time 6D object pose estimation framework, named G2L-Net.

38, TITLE:      Adversarial Generation of Continuous Implicit Shape Representations
http://arxiv.org/abs/2002.00349
AUTHORS:        Marian Kleineberg ; Matthias Fey ; Frank Weichert
COMMENTS:       Published in Eurographics 2020 - Short Papers
HIGHLIGHT:      This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations.

39, TITLE:      Convolutional Neural Networks with Dynamic Regularization
http://arxiv.org/abs/1909.11862
AUTHORS:        Yi Wang ; Zhen-Peng Bian ; Junhui Hou ; Lap-Pui Chau
COMMENTS:       7 pages. extended to DenseNet for Section III.C and Section IV.B.3. conducted experiments on ImageNet. added the comparison with DropBlock
HIGHLIGHT:      In this paper, we propose a dynamic regularization method for CNNs.

40, TITLE:      $Π-$nets: Deep Polynomial Neural Networks
http://arxiv.org/abs/2003.03828
AUTHORS:        Grigorios G. Chrysos ; Stylianos Moschoglou ; Giorgos Bouritsas ; Yannis Panagakis ; Jiankang Deng ; Stefanos Zafeiriou
COMMENTS:       Accepted in CVPR 2020
HIGHLIGHT:      In this paper, we propose $\Pi$-Nets, a new class of DCNNs.

41, TITLE:      Accelerating CNN Training by Pruning Activation Gradients
http://arxiv.org/abs/1908.00173
AUTHORS:        Xucheng Ye ; Pengcheng Dai ; Junyu Luo ; Xin Guo ; Yingjie Qi ; Jianlei Yang ; Yiran Chen ; Weisheng Zhao
HIGHLIGHT:      Hence, we consider pruning these very small gradients randomly to accelerate CNN training according to the statistical distribution of activation gradients.

42, TITLE:      Unsupervised Learning of Intrinsic Structural Representation Points
http://arxiv.org/abs/2003.01661
AUTHORS:        Nenglun Chen ; Lingjie Liu ; Zhiming Cui ; Runnan Chen ; Duygu Ceylan ; Changhe Tu ; Wenping Wang
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      We present a simple yet interpretable unsupervised method for learning a new structural representation in the form of 3D structure points.

43, TITLE:      Neural Mesh Refiner for 6-DoF Pose Estimation
http://arxiv.org/abs/2003.07561
AUTHORS:        Di Wu ; Yihao Chen ; Xianbiao Qi ; Yongjian Yu ; Weixuan Chen ; Rong Xiao
HIGHLIGHT:      This paper bridges the gap between 2D mask generation and 3D location prediction via a differentiable neural mesh renderer.

44, TITLE:      Learning Object Permanence from Video
http://arxiv.org/abs/2003.10469
AUTHORS:        Aviv Shamsian ; Ofri Kleinfeld ; Amir Globerson ; Gal Chechik
HIGHLIGHT:      Here we introduce the setup of learning Object Permanence from data.