2020.03.19.txt

==========New Papers==========
1, TITLE:       Combinatory Chemistry: Towards a Simple Model of Emergent Evolution
http://arxiv.org/abs/2003.07916
AUTHORS:        Germán Kruszewski ; Tomas Mikolov
HIGHLIGHT:      To tackle this challenge, here we introduce Combinatory Chemistry, an Algorithmic Artificial Chemistry based on a simple computational paradigm named Combinatory Logic.

2, TITLE:       Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study
http://arxiv.org/abs/2003.08001
AUTHORS:        Farahnaz Akrami ; Mohammed Samiul Saeef ; Qingheng Zhang ; Wei Hu ; Chengkai Li
COMMENTS:       accepted to SIGMOD 2020
HIGHLIGHT:      This paper is the first systematic study with the main objective of assessing the true effectiveness of embedding models when the unrealistic triples are removed.

3, TITLE:       Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá
http://arxiv.org/abs/2003.08370
AUTHORS:        David Ifeoluwa Adelani ; Michael A. Hedderich ; Dawei Zhu ; Esther van den Berg ; Dietrich Klakow
COMMENTS:       Accepted to ICLR 2020 Workshop
HIGHLIGHT:      In this work, we perform named entity recognition for Hausa and Yor\`ub\'a, two languages that are widely spoken in several developing countries.

4, TITLE:       Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization
http://arxiv.org/abs/2003.08004
AUTHORS:        Haiyang Xu ; Yun Wang ; Kun Han ; Baochang Ma ; Junwen Chen ; Xiangang Li
COMMENTS:       ICASSP 2020
HIGHLIGHT:      In this paper, we propose to use a graph to connect the parsing trees from the sentences in a document and utilize the stacked graph convolutional networks (GCNs) to learn the syntactic representation for a document.

5, TITLE:       X-Stance: A Multilingual Multi-Target Dataset for Stance Detection
http://arxiv.org/abs/2003.08385
AUTHORS:        Jannis Vamvas ; Rico Sennrich
COMMENTS:       Data and code are available at https://github.com/ZurichNLP/xstance
HIGHLIGHT:      Unlike stance detection models that have specific target issues, we use the dataset to train a single model on all the issues.

6, TITLE:       Cross Lingual Cross Corpus Speech Emotion Recognition
http://arxiv.org/abs/2003.07996
AUTHORS:        Shivali Goel ; Homayoon Beigi
COMMENTS:       7 pages, 2 figures
HIGHLIGHT:      This paper presents results for speech emotion recognition for 4 languages in both single corpus and cross corpus setting.

7, TITLE:       TTTTTackling WinoGrande Schemas
http://arxiv.org/abs/2003.08380
AUTHORS:        Sheng-Chieh Lin ; Jheng-Hong Yang ; Rodrigo Nogueira ; Ming-Feng Tsai ; Chuan-Ju Wang ; Jimmy Lin
HIGHLIGHT:      We applied the T5 sequence-to-sequence model to tackle the AI2 WinoGrande Challenge by decomposing each example into two input text strings, each containing a hypothesis, and using the probabilities assigned to the "entailment" token as a score of the hypothesis.

8, TITLE:       Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
http://arxiv.org/abs/2003.08061
AUTHORS:        Zezheng Wang ; Zitong Yu ; Chenxu Zhao ; Xiangyu Zhu ; Yunxiao Qin ; Qiusheng Zhou ; Feng Zhou ; Zhen Lei
COMMENTS:       Accepted by CVPR2020 (oral)
HIGHLIGHT:      In contrast, we design a new approach to detect presentation attacks from multiple frames based on two insights: 1) detailed discriminative clues (e.g., spatial gradient magnitude) between living and spoofing face may be discarded through stacked vanilla convolutions, and 2) the dynamics of 3D moving faces provide important clues in detecting the spoofing faces. To assess the efficacy of our method, we also collect a Double-modal Anti-spoofing Dataset (DMAD) which provides actual depth for each sample.

9, TITLE:       Multi-task Learning with Coarse Priors for Robust Part-aware Person Re-identification
http://arxiv.org/abs/2003.08069
AUTHORS:        Changxing Ding ; Kan Wang ; Pengfei Wang ; Dacheng Tao
COMMENTS:       submitted in September, 2019
HIGHLIGHT:      In this paper, we present a robust, compact, and easy-to-use method called the Multi-task Part-aware Network (MPN), which is designed to extract semantically aligned part-level features from pedestrian images.

10, TITLE:      OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems
http://arxiv.org/abs/2003.08056
AUTHORS:        Changhee Won ; Hochang Seok ; Zhaopeng Cui ; Marc Pollefeys ; Jongwoo Lim
COMMENTS:       accepted by ICRA 2020
HIGHLIGHT:      In this paper, we present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras, which has a 360 degrees coverage of stereo observations of the environment.

11, TITLE:      OpenGAN: Open Set Generative Adversarial Networks
http://arxiv.org/abs/2003.08074
AUTHORS:        Luke Ditria ; Benjamin J. Meyer ; Tom Drummond
HIGHLIGHT:      We propose an open set GAN architecture (OpenGAN) that is conditioned per-input sample with a feature embedding drawn from a metric space.

12, TITLE:      Estimation of Orofacial Kinematics in Parkinson's Disease: Comparison of 2D and 3D Markerless Systems for Motion Tracking
http://arxiv.org/abs/2003.08048
AUTHORS:        Diego L. Guarin ; Aidan Dempster ; Andrea Bandini ; Yana Yunusova ; Babak Taati
COMMENTS:       4 pages, 1 table
HIGHLIGHT:      The objective of this paper was to evaluate if depth cameras are needed to differentiate between healthy controls and PD patients based on features extracted from orofacial kinematics.

13, TITLE:      Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
http://arxiv.org/abs/2003.08073
AUTHORS:        Moab Arar ; Yiftach Ginger ; Dov Danon ; Ilya Leizerson ; Amit Bermano ; Daniel Cohen-Or
HIGHLIGHT:      In this work, we bypass the difficulties of developing cross-modality similarity measures, by training an image-to-image translation network on the two input modalities.

14, TITLE:      Federated Visual Classification with Real-World Data Distribution
http://arxiv.org/abs/2003.08082
AUTHORS:        Tzu-Ming Harry Hsu ; Hang Qi ; Matthew Brown
HIGHLIGHT:      In this work, we characterize the effect these real-world data distributions have on distributed learning, using as a benchmark the standard Federated Averaging (FedAvg) algorithm. To do so, we introduce two new large-scale datasets for species and landmark classification, with realistic per-user data splits that simulate real-world edge learning scenarios.

15, TITLE:      Deliberation Model Based Two-Pass End-to-End Speech Recognition
http://arxiv.org/abs/2003.07962
AUTHORS:        Ke Hu ; Tara N. Sainath ; Ruoming Pang ; Rohit Prabhavalkar
HIGHLIGHT:      In this work, we propose to attend to both acoustics and first-pass hypotheses using a deliberation network.

16, TITLE:      Rethinking Object Detection in Retail Stores
http://arxiv.org/abs/2003.08230
AUTHORS:        Yuanqiang Cai ; Longyin Wen ; Libo Zhang ; Dawei Du ; Weiqiang Wang ; Pengfei Zhu
HIGHLIGHT:      In this paper, we propose a new task, ie, simultaneously object localization and counting, abbreviated as Locount, which requires algorithms to localize groups of objects of interest with the number of instances.

17, TITLE:      High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
http://arxiv.org/abs/2003.08177
AUTHORS:        Guan'an Wang ; Shuo Yang ; Huanyu Liu ; Zhicheng Wang ; Yang Yang ; Shuliang Wang ; Gang Yu ;  ErjinZhou ; Jian Sun
COMMENTS:       accepted by CVPR'20
HIGHLIGHT:      In this paper, we propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment.

18, TITLE:      3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels
http://arxiv.org/abs/2003.08162
AUTHORS:        Qi Zhang ; Antoni B. Chan
COMMENTS:       8 pages, 5 figures, AAAI Conference on Artificial Intelligence, AAAI, New York, Feb 2020
HIGHLIGHT:      Unlike MVMS, we propose to solve the multi-view crowd counting task through 3D feature fusion with 3D scene-level density maps, instead of the 2D ground-plane ones.

19, TITLE:      Detection of Pitt-Hopkins Syndrome based on morphological facial features
http://arxiv.org/abs/2003.08229
AUTHORS:        Elena D'Amato ; Constantino Carlos Reyes-Aldasoro ; Maria Felicia Faienza ; Marcella Zollino
COMMENTS:       Submitted to MIUA 2020
HIGHLIGHT:      This work describes an automatic methodology to discriminate between individuals with the genetic disorder Pitt-Hopkins syndrome (PTHS), and healthy individuals.

20, TITLE:      SwapText: Image Based Texts Transfer in Scenes
http://arxiv.org/abs/2003.08152
AUTHORS:        Qiangpeng Yang ; Hongsheng Jin ; Jun Huang ; Wei Lin
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      In this work, we present SwapText, a three-stage framework to transfer texts across scene images.

21, TITLE:      Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training
http://arxiv.org/abs/2003.08272
AUTHORS:        Ernie Chang ; David Ifeoluwa Adelani ; Xiaoyu Shen ; Vera Demberg
COMMENTS:       Accepted to Workshop at ICLR 2020
HIGHLIGHT:      In this work, we develop techniques targeted at bridging the gap between Pidgin English and English in the context of natural language generation.

22, TITLE:      Calibration of Pre-trained Transformers
http://arxiv.org/abs/2003.07892
AUTHORS:        Shrey Desai ; Greg Durrett
HIGHLIGHT:      We focus on BERT and RoBERTa in this work, and analyze their calibration across three tasks: natural language inference, paraphrase detection, and commonsense reasoning.

23, TITLE:      Gender Representation in Open Source Speech Resources
http://arxiv.org/abs/2003.08132
AUTHORS:        Mahault Garnerin ; Solange Rossato ; Laurent Besacier
COMMENTS:       accepted to LREC2020
HIGHLIGHT:      We address transparency and fairness in spoken language systems by proposing a study about gender representation in speech resources available through the Open Speech and Language Resource platform.

24, TITLE:      Pre-trained Models for Natural Language Processing: A Survey
http://arxiv.org/abs/2003.08271
AUTHORS:        Xipeng Qiu ; Tianxiang Sun ; Yige Xu ; Yunfan Shao ; Ning Dai ; Xuanjing Huang
COMMENTS:       Invited Review of Science China Technological Sciences
HIGHLIGHT:      In this survey, we provide a comprehensive review of PTMs for NLP.

25, TITLE:      Object-Based Image Coding: A Learning-Driven Revisit
http://arxiv.org/abs/2003.08033
AUTHORS:        Qi Xia ; Haojie Liu ; Zhan Ma
COMMENTS:       ICME2020
HIGHLIGHT:      To attack this, we have proposed to apply the element-wise masking and compression by devising an object segmentation network for image layer decomposition, and parallel convolution-based neural image compression networks to process masked foreground objects and background scene separately.

26, TITLE:      Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
http://arxiv.org/abs/2003.08040
AUTHORS:        Zhonghao Wang ; Mo Yu ; Yunchao Wei ; Rogerior Feris ; Jinjun Xiong ; Wen-mei Hwu ; Thomas S. Huang ; Honghui Shi
COMMENTS:       CVPR 2020
HIGHLIGHT:      We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.

27, TITLE:      Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario
http://arxiv.org/abs/2003.08024
AUTHORS:        Yu Tian ; Kunbo Zhang ; Leyuan Wang ; Zhenan Sun
COMMENTS:       14pages,8figures
HIGHLIGHT:      In this paper, we present a face anti-spoofing method in a real-world scenario by automatic learning the physical characteristics in polarization images of a real face compared to a deceptive attack.

28, TITLE:      STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition
http://arxiv.org/abs/2003.08042
AUTHORS:        Xu Li ; Jingwen Wang ; Lin Ma ; Kaihao Zhang ; Fengzong Lian ; Zhanhui Kang ; Jinjun Wang
HIGHLIGHT:      In this paper, we present a novel Spatio-Temporal Hybrid Convolution Network (denoted as "STH") which simultaneously encodes spatial and temporal video information with a small parameter cost.

29, TITLE:      MUTATT: Visual-Textual Mutual Guidance for Referring Expression Comprehension
http://arxiv.org/abs/2003.08027
AUTHORS:        Shuai Wang ; Fan Lyu ; Wei Feng ; Song Wang
HIGHLIGHT:      In this paper, we argue that for REC the referring expression and the target region are semantically correlated and subject, location and relationship consistency exist between vision and language.On top of this, we propose a novel approach called MutAtt to construct mutual guidance between vision and language, which treat vision and language equally thus yield compact information matching.

30, TITLE:      Capsule GAN Using Capsule Network for Generator Architecture
http://arxiv.org/abs/2003.08047
AUTHORS:        Kanako Marusaki ; Hiroshi Watanabe
COMMENTS:       7 pages and 8 figures
HIGHLIGHT:      This paper introduces two approaches to use Capsule Network in the generator.

31, TITLE:      Anchor & Transform: Learning Sparse Representations of Discrete Objects
http://arxiv.org/abs/2003.08197
AUTHORS:        Paul Pu Liang ; Manzil Zaheer ; Yuan Wang ; Amr Ahmed
HIGHLIGHT:      In this paper, we design a Bayesian nonparametric prior for embeddings that encourages sparsity and leverages natural groupings among objects.

32, TITLE:      An Algorithm for Computing a Minimal Comprehensive Gröbner\, Basis of a Parametric Polynomial System
http://arxiv.org/abs/2003.07957
AUTHORS:        Deepak Kapur ; Yiming Yang
COMMENTS:       8 pages
HIGHLIGHT:      The algorithm has been implemented and successfully tried on many examples from the literature.

33, TITLE:      How social feedback processing in the brain shapes collective opinion processes in the era of social media
http://arxiv.org/abs/2003.08154
AUTHORS:        Sven Banisch ; Felix Gaisbauer ; Eckehard Olbrich
COMMENTS:       Odycceus Research (www.odycceus.eu)
HIGHLIGHT:      Drawing on recent neuro-scientific insights into the processing of social feedback, we develop a theoretical model that allows to address these questions.

34, TITLE:      Sequential Forecasting of 100,000 Points
http://arxiv.org/abs/2003.08376
AUTHORS:        Xinshuo Weng ; Jianren Wang ; Sergey Levine ; Kris Kitani ; Nicholas Rhinehart
COMMENTS:       Code will be available at https://github.com/xinshuoweng/SPCSF
HIGHLIGHT:      In this work, we study the problem of future prediction at the level of 3D scenes, represented by point clouds captured by a LiDAR sensor, i.e., directly learning to forecast the evolution of >100,000 points that comprise a complete scene.

35, TITLE:      DLow: Diversifying Latent Flows for Diverse Human Motion Prediction
http://arxiv.org/abs/2003.08386
AUTHORS:        Ye Yuan ; Kris Kitani
COMMENTS:       Video: https://youtu.be/64OEdSadb00
HIGHLIGHT:      To address these problems, we propose a novel sampling method, Diversifying Latent Flows (DLow), to produce a diverse set of samples from a pretrained deep generative model.

36, TITLE:      In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization
http://arxiv.org/abs/2003.08375
AUTHORS:        Amir Rahimi ; Amirreza Shaban ; Thalaiyasingam Ajanthan ; Richard Hartley ; Byron Boots
COMMENTS:       22 pages, 7 figures
HIGHLIGHT:      In this work, we argue that learning only an objectness function is a weak form of knowledge transfer and propose to learn a classwise pairwise similarity function that directly compares two input proposals as well.

37, TITLE:      Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
http://arxiv.org/abs/2003.08367
AUTHORS:        Pratul P. Srinivasan ; Ben Mildenhall ; Matthew Tancik ; Jonathan T. Barron ; Richard Tucker ; Noah Snavely
COMMENTS:       CVPR 2020. Project page: https://people.eecs.berkeley.edu/~pratul/lighthouse/
HIGHLIGHT:      We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair.

38, TITLE:      Constraints in Developing a Complete Bengali Optical Character Recognition System
http://arxiv.org/abs/2003.08384
AUTHORS:        Abu Saleh Md. Abir ; Sanjana Rahman ; Samia Ellin ; Maisha Farzana ; Md Hridoy Manik ; Chowdhury Rafeed Rahman
HIGHLIGHT:      The aim of this research is to analyze the challenges prevalent in developing a Bengali OCR system through robust literature review and implementation.

39, TITLE:      Weakly Supervised PET Tumor Detection UsingClass Response
http://arxiv.org/abs/2003.08337
AUTHORS:        Amine Amyar ; Romain Modzelewski ; Pierre Vera ; Vincent Morard ; Su Ruan
COMMENTS:       8 pages, 2 figures, submitted to MICCAI 2020
HIGHLIGHT:      In this paper, we present a novel approach to locate different type of lesions in positron emission tomography (PET) images using only a class label at the image-level.

40, TITLE:      Thermodynamic Cost of Edge Detection in Artificial Neural Network(ANN)-Based Processors
http://arxiv.org/abs/2003.08196
AUTHORS:        Seçkin Barışık ; İlke Ercan
HIGHLIGHT:      In this work, we study architectural-level contributions to energy dissipation in Artificial Neural Network (ANN)-based processors that are trained to perform edge detection task.

41, TITLE:      An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
http://arxiv.org/abs/2003.08273
AUTHORS:        Ya Lu ; Thomai Stathopoulou ; Maria F. Vasiloglou ; Stergios Christodoulidis ; Zeno Stanga ; Stavroula Mougiakakou
HIGHLIGHT:      In this paper, we propose a novel system based on artificial intelligence (AI) to accurately estimate nutrient intake, by simply processing RGB Depth (RGB-D) image pairs captured before and after meal consumption.

42, TITLE:      LRC-Net: Learning Discriminative Features on Point Clouds by EncodingLocal Region Contexts
http://arxiv.org/abs/2003.08240
AUTHORS:        Xinhai Liu ; Zhizhong Han ; Fangzhou Hong ; Yu-Shen Liu ; Matthias Zwicker
COMMENTS:       To be published at GMP2020
HIGHLIGHT:      To address this issue, we present a novel Local-Region-Context Network (LRC-Net), to learn discriminative features on point clouds by encoding the fine-grained contexts inside and among local regions simultaneously.

43, TITLE:      CAFENet: Class-Agnostic Few-Shot Edge Detection Network
http://arxiv.org/abs/2003.08235
AUTHORS:        Young-Hyun Park ; Jun Seo ; Jaekyun Moon
COMMENTS:       15 pages, 6 figures
HIGHLIGHT:      We tackle a novel few-shot learning challenge, which we call few-shot semantic edge detection, aiming to localize crisp boundaries of novel categories using only a few labeled samples. Since there is no existing dataset for few-shot semantic edge detection, we construct two new datasets, FSE-1000 and SBD-$5^i$, and evaluate the performance of the proposed CAFENet on them.

44, TITLE:      Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels
http://arxiv.org/abs/2003.08264
AUTHORS:        Donghyun Kim ; Kuniaki Saito ; Tae-Hyun Oh ; Bryan A. Plummer ; Stan Sclaroff ; Kate Saenko
HIGHLIGHT:      Existing unsupervised domain adaptation methods aim to transfer knowledge from a label-rich source domain to an unlabeled target domain.

45, TITLE:      PIC: Permutation Invariant Convolution for Recognizing Long-range Activities
http://arxiv.org/abs/2003.08275
AUTHORS:        Noureldien Hussein ; Efstratios Gavves ; Arnold W. M. Smeulders
HIGHLIGHT:      This paper presents PIC, Permutation Invariant Convolution, a novel neural layer to model the temporal structure of long-range activities.

46, TITLE:      Fixing the train-test resolution discrepancy: FixEfficientNet
http://arxiv.org/abs/2003.08237
AUTHORS:        Hugo Touvron ; Andrea Vedaldi ; Matthijs Douze ; Hervé Jégou
HIGHLIGHT:      This note complements the paper "Fixing the train-test resolution discrepancy" that introduced the FixRes method.

47, TITLE:      AMIL: Adversarial Multi Instance Learning for Human Pose Estimation
http://arxiv.org/abs/2003.08002
AUTHORS:        Pourya Shamsolmoali ; Masoumeh Zareapoor ; Huiyu Zhou ; Jie Yang
HIGHLIGHT:      Instead, we propose generative adversarial networks as our learning model in which we design two residual multiple instance learning (MIL) models with the identical architecture, one is used as the generator and the other one is used as the discriminator.

48, TITLE:      Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images
http://arxiv.org/abs/2003.07999
AUTHORS:        Donghao Zhang ; Siqi Liu ; Shikha Chaganti ; Eli Gibson ; Zhoubing Xu ; Sasa Grbic ; Weidong Cai ; Dorin Comaniciu
HIGHLIGHT:      In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.

49, TITLE:      Applying r-spatiogram in object tracking for occlusion handling
http://arxiv.org/abs/2003.08021
AUTHORS:        Niloufar Salehi Dastjerdi ; M. Omair Ahmad
HIGHLIGHT:      In this paper, we use the basic idea of many trackers which consists of three main components of the reference model, i.e., object modeling, object detection and localization, and model updating.

50, TITLE:      ScanSSD: Scanning Single Shot Detector for Mathematical Formulas in PDF Document Images
http://arxiv.org/abs/2003.08005
AUTHORS:        Parag Mali ; Puneeth Kukkadapu ; Mahshad Mahdavi ; Richard Zanibbi
COMMENTS:       8 pages, 7 figures
HIGHLIGHT:      We introduce the Scanning Single Shot Detector (ScanSSD) for locating math formulas offset from text and embedded in textlines.

51, TITLE:      A Dynamic Reduction Network for Point Clouds
http://arxiv.org/abs/2003.08013
AUTHORS:        Lindsey Gray ; Thomas Klijnsma ; Shamik Ghosh
COMMENTS:       4 pages, 2 figures, to be updated
HIGHLIGHT:      In this paper, a dynamic graph formulation of pooling is introduced that removes the need for predetermined graph structure.

52, TITLE:      Neuroevolution of Self-Interpretable Agents
http://arxiv.org/abs/2003.08165
AUTHORS:        Yujin Tang ; Duong Nguyen ; David Ha
HIGHLIGHT:      Motivated by selective attention, we study the properties of artificial agents that perceive the world through the lens of a self-attention bottleneck.

53, TITLE:      Signature restriction for polymorphic algebraic effects
http://arxiv.org/abs/2003.08138
AUTHORS:        Taro Sekiyama ; Takeshi Tsukada ; Atsushi Igarashi
HIGHLIGHT:      To formalize our idea, we employ algebraic effects and handlers, where an effect interface is given by a set of operations coupled with type signatures.

54, TITLE:      Hardness of Bounded Distance Decoding on Lattices in $\ell_p$ Norms
http://arxiv.org/abs/2003.07903
AUTHORS:        Huck Bennett ; Chris Peikert
HIGHLIGHT:      Our reductions rely on a special family of "locally dense" lattices in $\ell_{p}$ norms, which we construct by modifying the integer-lattice sparsification technique of Aggarwal and Stephens-Davidowitz (STOC 2018).

55, TITLE:      A Generalization of Self-Improving Algorithms
http://arxiv.org/abs/2003.08329
AUTHORS:        Siu-Wing Cheng ; Man-Kwun Chiu ; Kai Jin ; Man Ting Wong
HIGHLIGHT:      In this paper, we allow dependence among the $x_i$'s under the \emph{group product distribution}.

56, TITLE:      Tatamibari is NP-complete
http://arxiv.org/abs/2003.08331
AUTHORS:        Aviv Adler ; Jeffrey Bosboom ; Erik D. Demaine ; Martin L. Demaine ; Quanquan C. Liu ; Jayson Lynch
COMMENTS:       26 pages, 21 figures
HIGHLIGHT:      Along the way, we introduce a gadget framework for proving hardness of similar puzzles involving area coverage, and show that it applies to an existing NP-hardness proof for Spiral Galaxies.

57, TITLE:      Breast Cancer Detection Using Convolutional Neural Networks
http://arxiv.org/abs/2003.07911
AUTHORS:        Simon Hadush ; Yaecob Girmay ; Abiot Sinamo ; Gebrekirstos Hagos
HIGHLIGHT:      Deep learning techniques are revolutionizing the field of medical image analysis and hence in this study, we proposed Convolutional Neural Networks (CNNs) for breast mass detection so as to minimize the overheads of manual analysis.

58, TITLE:      Deep connections between learning from limited labels & physical parameter estimation -- inspiration for regularization
http://arxiv.org/abs/2003.07908
AUTHORS:        Bas Peters
HIGHLIGHT:      We show that explicit regularization of model parameters in PDE constrained optimization translates to regularization of the network output.

59, TITLE:      Ford Multi-AV Seasonal Dataset
http://arxiv.org/abs/2003.07969
AUTHORS:        Siddharth Agarwal ; Ankit Vora ; Gaurav Pandey ; Wayne Williams ; Helen Kourous ; James McBride
COMMENTS:       7 pages, 7 figures, Submitted to International Journal of Robotics Research (IJRR), Visit website at https://avdata.ford.com
HIGHLIGHT:      This paper presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18.

60, TITLE:      The Future of Digital Health with Federated Learning
http://arxiv.org/abs/2003.08119
AUTHORS:        Nicola Rieke ; Jonny Hancox ; Wenqi Li ; Fausto Milletari ; Holger Roth ; Shadi Albarqouni ; Spyridon Bakas ; Mathieu N. Galtier ; Bennett Landman ; Klaus Maier-Hein ; Sebastien Ourselin ; Micah Sheller ; Ronald M. Summers ; Andrew Trask ; Daguang Xu ; Maximilian Baust ; M. Jorge Cardoso
HIGHLIGHT:      This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.

61, TITLE:      From Statistical Relational to Neuro-Symbolic Artificial Intelligence
http://arxiv.org/abs/2003.08316
AUTHORS:        Luc De Raedt ; Sebastijan Dumančić ; Robin Manhaeve ; Giuseppe Marra
HIGHLIGHT:      Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.

62, TITLE:      Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images
http://arxiv.org/abs/2003.08124
AUTHORS:        Hang Zhou ; Jihao Liu ; Ziwei Liu ; Yu Liu ; Xiaogang Wang
COMMENTS:       To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. Code and models are available at: https://github.com/Hangz-nju-cuhk/Rotate-and-Render
HIGHLIGHT:      To overcome these challenges, we propose a novel unsupervised framework that can synthesize photo-realistic rotated faces using only single-view image collections in the wild.

63, TITLE:      Transformer Networks for Trajectory Forecasting
http://arxiv.org/abs/2003.08111
AUTHORS:        Francesco Giuliari ; Irtiza Hasan ; Marco Cristani ; Fabio Galasso
COMMENTS:       18 pages, 3 figures
HIGHLIGHT:      We question the use of the LSTM models and propose the novel use of Transformer Networks for trajectory forecasting.

64, TITLE:      Scene Text Recognition via Transformer
http://arxiv.org/abs/2003.08077
AUTHORS:        Xinjie Feng ; Hongxun Yao ; Yuankai Yi ; Jun Zhang ; Shengping Zhang
HIGHLIGHT:      In this paper, we find that the rectification is completely unnecessary.

65, TITLE:      A Driver Fatigue Recognition Algorithm Based on Spatio-Temporal Feature Sequence
http://arxiv.org/abs/2003.08134
AUTHORS:        Chen Zhang ; Xiaobo Lu ; Zhiliang Huang
HIGHLIGHT:      This paper designs a real-time fatigue state recognition algorithm based on spatio-temporal feature sequence, which can be mainly applied to the scene of fatigue driving recognition.

66, TITLE:      Task-Adaptive Clustering for Semi-Supervised Few-Shot Classification
http://arxiv.org/abs/2003.08221
AUTHORS:        Jun Seo ; Sung Whan Yoon ; Jaekyun Moon
COMMENTS:       15 pages, 5 figures
HIGHLIGHT:      In this work, we propose a few-shot learner that can work well under the semi-supervised setting where a large portion of training data is unlabeled.

67, TITLE:      Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted $\ell_1$-$\ell_1$ Minimization: Architecture Design and Generalization Analysis
http://arxiv.org/abs/2003.08334
AUTHORS:        Huynh Van Luong ; Boris Joukovsky ; Nikos Deligiannis
COMMENTS:       Pre-print: 14 pages
HIGHLIGHT:      We apply the proposed reweighted-RNN to the problem of video frame reconstruction from low-dimensional measurements, that is, sequential frame reconstruction.

68, TITLE:      Deep Quaternion Features for Privacy Protection
http://arxiv.org/abs/2003.08365
AUTHORS:        Hao Zhang ; Yiting Chen ; Liyao Xiang ; Haotian Ma ; Jie Shi ; Quanshi Zhang
HIGHLIGHT:      We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input information.

69, TITLE:      Learning Nonlinear Loop Invariants with Gated Continuous Logic Networks
http://arxiv.org/abs/2003.07959
AUTHORS:        Jianan Yao ; Gabriel Ryan ; Justin Wong ; Suman Jana ; Ronghui Gu
HIGHLIGHT:      In this paper, we introduce a new neural architecture for general SMT learning, the Gated Continuous Logic Network (G-CLN), and apply it to nonlinear loop invariant learning.

70, TITLE:      Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method
http://arxiv.org/abs/2003.08225
AUTHORS:        Yuan Gong ; Jian Yang ; Christian Poellabauer
HIGHLIGHT:      In this paper, we introduce a novel neural network-based replay attack detection model that further leverages spatial information of multi-channel audio and is able to significantly improve the replay attack detection performance.

71, TITLE:      On the Distribution of Minima in Intrinsic-Metric Rotation Averaging
http://arxiv.org/abs/2003.08310
AUTHORS:        Kyle Wilson ; David Bindel
COMMENTS:       To be published in CVPR2020
HIGHLIGHT:      In this paper, we study the spatial distribution of local minima.

72, TITLE:      A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations
http://arxiv.org/abs/2003.08332
AUTHORS:        Karim Makki ; Amine Bohi ; Augustin C. Ogier ; Marc-Emmanuel Bellemare
HIGHLIGHT:      In this paper, we propose a method for characterizing 3D shapes from point clouds and we show a direct application on a study of organ temporal deformations.

73, TITLE:      Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
http://arxiv.org/abs/2003.08284
AUTHORS:        Weikai Tan ; Nannan Qin ; Lingfei Ma ; Ying Li ; Jing Du ; Guorong Cai ; Ke Yang ; Jonathan Li
HIGHLIGHT:      This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation.

74, TITLE:      Collaborative Video Object Segmentation by Foreground-Background Integration
http://arxiv.org/abs/2003.08333
AUTHORS:        Zongxin Yang ; Yunchao Wei ; Yi Yang
HIGHLIGHT:      In this paper, we investigate the principles of embedding learning between the given reference and the predicted sequence to tackle the challenging semi-supervised video object segmentation.

75, TITLE:      Multi-View Optimization of Local Feature Geometry
http://arxiv.org/abs/2003.08348
AUTHORS:        Mihai Dusmanu ; Johannes L. Schönberger ; Marc Pollefeys
COMMENTS:       27 pages, 11 figures, 6 tables
HIGHLIGHT:      In this work, we address the problem of refining the geometry of local image features from multiple views without known scene or camera geometry.

76, TITLE:      DeepCap: Monocular Human Performance Capture Using Weak Supervision
http://arxiv.org/abs/2003.08325
AUTHORS:        Marc Habermann ; Weipeng Xu ; Michael Zollhoefer ; Gerard Pons-Moll ; Christian Theobalt
HIGHLIGHT:      We propose a novel deep learning approach for monocular dense human performance capture.

77, TITLE:      Event Probability Mask (EPM) and Event Denoising Convolutional NeuralNetwork (EDnCNN) for Neuromorphic Cameras
http://arxiv.org/abs/2003.08282
AUTHORS:        R. Wes Baldwin ; Mohammed Almatrafi ; Vijayan Asari ; Keigo Hirakawa
COMMENTS:       submitted to CVPR 2020
HIGHLIGHT:      This paper presents a novel method for labeling real-world neuromorphic camera sensor data by calculating the likelihood of generating an event at each pixel within a short time window, which we refer to as "event probability mask" or EPM. We provide the first dataset (DVSNOISE20) of real-world labeled neuromorphic camera events for noise removal.

78, TITLE:      Triplet Permutation Method for Deep Learning of Single-Shot Person Re-Identification
http://arxiv.org/abs/2003.08303
AUTHORS:        M. J. Gómez-Silva ; J. M. Armingol ; A. de la Escalera
HIGHLIGHT:      This paper formulates the Triplet Permutation method to generate multiple training sets, from a certain re-id dataset.

79, TITLE:      3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images
http://arxiv.org/abs/2003.07923
AUTHORS:        Cheryl Sital ; Tom Brosch ; Dominique Tio ; Alexander Raaijmakers ; Jürgen Weese
HIGHLIGHT:      This work investigates the potential of autoencoder-extracted features to improve segmentation with a CNN.

80, TITLE:      BrazilDAM: A Benchmark dataset for Tailings Dam Detection
http://arxiv.org/abs/2003.07948
AUTHORS:        Edemir Ferreira ; Matheus Brito ; Remis Balaniuk ; Mário S. Alvim ; Jefersson A. dos Santos
HIGHLIGHT:      In this work we present BrazilDAM, a novel public dataset based on Sentinel-2 and Landsat-8 satellite images covering all tailings dams cataloged by the Brazilian National Mining Agency (ANM).

81, TITLE:      Generalizing Face Representation with Unlabeled Data
http://arxiv.org/abs/2003.07936
AUTHORS:        Yichun Shi ; Anil K. Jain
HIGHLIGHT:      We present an approach to use such unlabeled faces to learn generalizable face representations, which can be viewed as an unsupervised domain generalization framework.

82, TITLE:      Watching the World Go By: Representation Learning from Unlabeled Videos
http://arxiv.org/abs/2003.07990
AUTHORS:        Daniel Gordon ; Kiana Ehsani ; Dieter Fox ; Ali Farhadi
HIGHLIGHT:      In this paper, we argue that videos offer this natural augmentation for free.

83, TITLE:      Getting to 99% Accuracy in Interactive Segmentation
http://arxiv.org/abs/2003.07932
AUTHORS:        Marco Forte ; Brian Price ; Scott Cohen ; Ning Xu ; François Pitié
COMMENTS:       Submitted for review to Signal Processing: Image Communication
HIGHLIGHT:      In this work, we interpret this plateau as the inability of current algorithms to sufficiently leverage each user interaction and also as the limitations of current training/testing datasets.

84, TITLE:      An End-to-end Framework For Low-Resolution Remote Sensing Semantic Segmentation
http://arxiv.org/abs/2003.07955
AUTHORS:        Matheus Barros Pereira ; Jefersson Alex dos Santos
HIGHLIGHT:      In this paper, we propose an end-to-end framework that unites a super-resolution and a semantic segmentation module in order to produce accurate thematic maps from LR inputs.

85, TITLE:      Segmentation of brain tumor on magnetic resonance imaging using a convolutional architecture
http://arxiv.org/abs/2003.07934
AUTHORS:        Miriam Zulema Jacobo ; Jose Mejia
COMMENTS:       6 pages, 5 figures
HIGHLIGHT:      Here we consider the problem brain tumor segmentation using a Deep learning architecture for use in tumor segmentation.

86, TITLE:      The State of Service Robots: Current Bottlenecks in Object Perception and Manipulation
http://arxiv.org/abs/2003.08151
AUTHORS:        S. Hamidreza Kasaei ; Jorik Melsen ; Floris van Beers ; Christiaan Steenkist ; Klemen Voncina
HIGHLIGHT:      In this paper, we review advances in service robots from object perception to complex object manipulation and shed a light on the current challenges and bottlenecks.

87, TITLE:      CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy
http://arxiv.org/abs/2003.08003
AUTHORS:        Xin Huang ; Stephen G. McGill ; Jonathan A. DeCastro ; Brian C. Williams ; Luke Fletcher ; John J. Leonard ; Guy Rosman
COMMENTS:       8 pages, 4 figures, 2 algorithms
HIGHLIGHT:      In this paper we devise a novel neural network regressor to estimate the utility distribution given the predictions.



==========Updates to Previous Papers==========
1, TITLE:       Evolving Structures in Complex Systems
http://arxiv.org/abs/1911.01086
AUTHORS:        Hugo Cisneros ; Josef Sivic ; Tomas Mikolov
COMMENTS:       IEEE Symposium Series on Computational Intelligence 2019 (IEEE SSCI 2019)
HIGHLIGHT:      In this paper we propose an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata.

2, TITLE:       From Abstractions to "Natural Languages" for Coordinating Planning Agents
http://arxiv.org/abs/1905.00517
AUTHORS:        Yu Zhang ; Li Wang
HIGHLIGHT:      In this paper, we investigate the automatic construction of these symbols from abstractions to form "natural languages" for such agents.

3, TITLE:       Distributional Semantics and Linguistic Theory
http://arxiv.org/abs/1905.01896
AUTHORS:        Gemma Boleda
COMMENTS:       22 pages, 4 figures; preprint version (minor modifications wrt previous version). When citing this article, please use the journal reference: Boleda, G. 2020. Distributional Semantics and Linguistic Theory. Annu. Rev. Linguist. 6:213-34
HIGHLIGHT:      The review aims at fostering greater cross-fertilization of theoretical and computational approaches to language, as a means to advance our collective knowledge of how it works.

4, TITLE:       Offensive Language Identification in Greek
http://arxiv.org/abs/2003.07459
AUTHORS:        Zeses Pitenis ; Marcos Zampieri ; Tharindu Ranasinghe
COMMENTS:       Accepted to LREC 2020
HIGHLIGHT:      To address this shortcoming, this paper presents the first Greek annotated dataset for offensive language identification: the Offensive Greek Tweet Dataset (OGTD).

5, TITLE:       JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset
http://arxiv.org/abs/2002.08397
AUTHORS:        Abhijeet Shenoi ; Mihir Patel ; JunYoung Gwak ; Patrick Goebel ; Amir Sadeghian ; Hamid Rezatofighi ; Roberto Martín-Martín ; Silvio Savarese
COMMENTS:       9 pages, 2 figures, 2 tables
HIGHLIGHT:      In this work we present JRMOT, a novel 3D MOT system that integrates information from 2D RGB images and 3D point clouds into a real-time performing framework. As part of our work, we release the JRDB dataset, a novel large scale 2D+3D dataset and benchmark annotated with over 2 million boxes and 3500 time consistent 2D+3D trajectories across 54 indoor and outdoor scenes.

6, TITLE:       Estimating People Flows to Better Count Them in Crowded Scenes
http://arxiv.org/abs/1911.10782
AUTHORS:        Weizhe Liu ; Mathieu Salzmann ; Pascal Fua
HIGHLIGHT:      In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing.

7, TITLE:       Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting
http://arxiv.org/abs/1911.11484
AUTHORS:        Weizhe Liu ; Mathieu Salzmann ; Pascal Fua
HIGHLIGHT:      In this paper, we investigate the effectiveness of existing attack strategies on crowd-counting networks, and introduce a simple yet effective pixel-wise detection mechanism.

8, TITLE:       SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
http://arxiv.org/abs/1910.08681
AUTHORS:        Qing Guo ; Xiaofei Xie ; Felix Juefei-Xu ; Lei Ma ; Zhongguo Li ; Wanli Xue ; Wei Feng ; Yang Liu
COMMENTS:       18 pages, 5 figures
HIGHLIGHT:      In this paper, we identify a new task for the adversarial attack to visual tracking: online generating imperceptible perturbations that mislead trackers along an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA).

9, TITLE:       Semantics-Aligned Representation Learning for Person Re-identification
http://arxiv.org/abs/1905.13143
AUTHORS:        Xin Jin ; Cuiling Lan ; Wenjun Zeng ; Guoqiang Wei ; Zhibo Chen
COMMENTS:       Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), code has been released
HIGHLIGHT:      In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs.

10, TITLE:      Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
http://arxiv.org/abs/1904.01189
AUTHORS:        Pengfei Zhang ; Cuiling Lan ; Wenjun Zeng ; Junliang Xing ; Jianru Xue ; Nanning Zheng
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this paper, we propose a simple yet effective semantics-guided neural network (SGN) for skeleton-based action recognition.

11, TITLE:      Learning Canonical Representations for Scene Graph to Image Generation
http://arxiv.org/abs/1912.07414
AUTHORS:        Roei Herzig ; Amir Bar ; Huijuan Xu ; Gal Chechik ; Trevor Darrell ; Amir Globerson
HIGHLIGHT:      In this work, we show that one limitation of current methods is their inability to capture semantic equivalence in graphs.

12, TITLE:      Overview of the TREC 2019 deep learning track
http://arxiv.org/abs/2003.07820
AUTHORS:        Nick Craswell ; Bhaskar Mitra ; Emine Yilmaz ; Daniel Campos ; Ellen M. Voorhees
HIGHLIGHT:      Possible explanations for this result are that we introduced large training data and we included deep models trained on such data in our judging pools, whereas some past studies did not have such training data or pooling. The document retrieval task has a corpus of 3.2 million documents with 367 thousand training queries, for which we generate a reusable test set of 43 queries. The passage retrieval task has a corpus of 8.8 million passages with 503 thousand training queries, for which we generate a reusable test set of 43 queries.

13, TITLE:      A Survey on Deep Learning for Named Entity Recognition
http://arxiv.org/abs/1812.09449
AUTHORS:        Jing Li ; Aixin Sun ; Jianglei Han ; Chenliang Li
COMMENTS:       20 pages, 12 figures, 3 tables
HIGHLIGHT:      In this paper, we provide a comprehensive review on existing deep learning techniques for NER.

14, TITLE:      The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service
http://arxiv.org/abs/1911.09969
AUTHORS:        Meng Chen ; Ruixue Liu ; Lei Shen ; Shaozu Yuan ; Jingyan Zhou ; Youzheng Wu ; Xiaodong He ; Bowen Zhou
COMMENTS:       This paper is accepted by LREC 2020 (Language Resource European Conference )
HIGHLIGHT:      In this paper, we construct a large-scale real scenario Chinese E-commerce conversation corpus, JDDC, with more than 1 million multi-turn dialogues, 20 million utterances, and 150 million words.

15, TITLE:      Dual Multi-head Co-attention for Multi-choice Reading Comprehension
http://arxiv.org/abs/2001.09415
AUTHORS:        Pengfei Zhu ; Hai Zhao ; Xiaoguang Li
HIGHLIGHT:      Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM and RACE, showing that in terms of strong Language Models, DUMA may still boost the model to reach new state-of-the-art performance.

16, TITLE:      GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model
http://arxiv.org/abs/2003.04360
AUTHORS:        Vaishali Ingale ; Pushpender Singh
HIGHLIGHT:      Here we proposed GenNet model, a neural network-based model.

17, TITLE:      Compound Probabilistic Context-Free Grammars for Grammar Induction
http://arxiv.org/abs/1906.10225
AUTHORS:        Yoon Kim ; Chris Dyer ; Alexander M. Rush
COMMENTS:       ACL 2019
HIGHLIGHT:      We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar.

18, TITLE:      Neural Blind Deconvolution Using Deep Priors
http://arxiv.org/abs/1908.02197
AUTHORS:        Dongwei Ren ; Kai Zhang ; Qilong Wang ; Qinghua Hu ; Wangmeng Zuo
COMMENTS:       Accepted to CVPR 2020. The source code is available at https://github.com/csdwren/SelfDeblur, and the supplementary file is at https://csdwren.github.io/papers/SelfDeblur_supp.pdf
HIGHLIGHT:      To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution.

19, TITLE:      Fast and Accurate 3D Hand Pose Estimation via Recurrent Neural Network for Capturing Hand Articulations
http://arxiv.org/abs/1911.07424
AUTHORS:        Cheol-hwan Yoo ; Seo-won Ji ; Yong-goo Shin ; Seung-wook Kim ; Sung-jea Ko
HIGHLIGHT:      In this paper, we propose a hierarchically-structured convolutional recurrent neural network (HCRNN) with six branches that estimate the 3D position of the palm and five fingers independently.

20, TITLE:      Scene Completeness-Aware Lidar Depth Completion for Driving Scenario
http://arxiv.org/abs/2003.06945
AUTHORS:        Cho-Ying Wu ; Ulrich Neumann
COMMENTS:       Under Review of IROS 2020
HIGHLIGHT:      In this paper we propose Scene Completeness-Aware Depth Completion (SADC) to complete raw lidar scans into dense depth maps with fine whole scene structures.

21, TITLE:      Bias-Reduced Hindsight Experience Replay with Virtual Goal Prioritization
http://arxiv.org/abs/1905.05498
AUTHORS:        Binyamin Manela ; Armin Biess
HIGHLIGHT:      In this paper, we present two improvements over the existing HER algorithm.

22, TITLE:      Joint Subcarrier and Power Allocation in NOMA: Optimal and Approximate Algorithms
http://arxiv.org/abs/1910.00510
AUTHORS:        Lou Salaün ; Marceau Coupechoux ; Chung Shue Chen
HIGHLIGHT:      To further reduce the complexity, we propose a fully polynomial-time approximation scheme called $\varepsilon$-JSPA.

23, TITLE:      Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings
http://arxiv.org/abs/1911.02357
AUTHORS:        Paul Bergmann ; Michael Fauser ; David Sattlegger ; Carsten Steger
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We introduce a powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images.

24, TITLE:      Probabilistic Tools for the Analysis of Randomized Optimization Heuristics
http://arxiv.org/abs/1801.06733
AUTHORS:        Benjamin Doerr
COMMENTS:       91 pages
HIGHLIGHT:      This chapter collects several probabilistic tools that proved to be useful in the analysis of randomized search heuristics.

25, TITLE:      Offline identification of surgical deviations in laparoscopic rectopexy
http://arxiv.org/abs/1909.10790
AUTHORS:        Arnaud Huaulmé ; Pierre Jannin ; Fabian Reche ; Jean-Luc Faucheron ; Alexandre Moreau-Gaudry ; Sandrine Voros
HIGHLIGHT:      In this paper, we have proposed a method enabling us to identify such deviations.

26, TITLE:      Evolutionary Neural Architecture Search for Retinal Vessel Segmentation
http://arxiv.org/abs/2001.06678
AUTHORS:        Zhun Fan ; Jiahong Wei ; Guijie Zhu ; Jiajie Mo ; Wenji Li
HIGHLIGHT:      In order to improve the performance of vessel segmentation and reduce the workload of manually designing neural network, we propose novel approach which applies neural architecture search (NAS) to optimize an encoder-decoder architecture for retinal vessel segmentation.

27, TITLE:      Maximum likelihood estimation for disk image parameters
http://arxiv.org/abs/1907.10557
AUTHORS:        Matwey V. Kornilov
COMMENTS:       13 pages, 4 figures
HIGHLIGHT:      We present a novel technique for estimating disk parameters (the centre and the radius) from its 2D image.

28, TITLE:      Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration
http://arxiv.org/abs/1911.07042
AUTHORS:        Robert Grupp ; Mathias Unberath ; Cong Gao ; Rachel Hegeman ; Ryan Murphy ; Clayton Alexander ; Yoshito Otake ; Benjamin McArthur ; Mehran Armand ; Russell Taylor
COMMENTS:       Revised article to address reviewer comments. Accepted to IPCAI 2020. Supplementary video at https://youtu.be/5AwGlNkcp9o and dataset/code at https://github.com/rg2/DeepFluoroLabeling-IPCAI2020
HIGHLIGHT:      We propose a method for fully automatic registration using annotations produced by a neural network.

29, TITLE:      MaskedFusion: Mask-based 6D Object Pose Estimation
http://arxiv.org/abs/1911.07771
AUTHORS:        Nuno Pereira ; Luís A. Alexandre
HIGHLIGHT:      MaskedFusion is a modular pipeline where each sub-task can have different methods that achieve the objective.

30, TITLE:      VeREFINE: Integrating Object Pose Verification with Iterative Physics-guided Refinement
http://arxiv.org/abs/1909.05730
AUTHORS:        Dominik Bauer ; Timothy Patten ; Markus Vincze
COMMENTS:       Revised version
HIGHLIGHT:      In this work, we propose to integrate hypotheses verification with object pose refinement guided by physics simulation.

31, TITLE:      Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis
http://arxiv.org/abs/2003.07603
AUTHORS:        Ruifeng Shi ; Deming Zhai ; Xianming Liu ; Junjun Jiang ; Wen Gao
HIGHLIGHT:      To overcome this problem, we propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.

32, TITLE:      CenterMask : Real-Time Anchor-Free Instance Segmentation
http://arxiv.org/abs/1911.06667
AUTHORS:        Youngwan Lee ; Jongyoul Park
COMMENTS:       CVPR 2020
HIGHLIGHT:      We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN.

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

34, TITLE:      PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening
http://arxiv.org/abs/1805.03371
AUTHORS:        Xiangyu Liu ; Yunhong Wang ; Qingjie Liu
COMMENTS:       Accepted by ICIP 2018
HIGHLIGHT:      In this paper, we propose PSGAN, a generative adversarial network (GAN) for remote sensing image pan-sharpening.

35, TITLE:      RVSDG: An Intermediate Representation for Optimizing Compilers
http://arxiv.org/abs/1912.05036
AUTHORS:        Nico Reissmann ; Jan Christian Meyer ; Helge Bahmann ; Magnus Själander
HIGHLIGHT:      We present the Regionalized Value State Dependence Graph (RVSDG) IR for optimizing compilers.

36, TITLE:      Kindly Bent to Free Us
http://arxiv.org/abs/1908.09681
AUTHORS:        Gabriel Radanne ; Hannes Saffrich ; Peter Thiemann
HIGHLIGHT:      We present Affe, an extension of ML that manages linearity and affinity properties using kinds and constrained types.

37, TITLE:      SOM-based DDoS Defense Mechanism using SDN for the Internet of Things
http://arxiv.org/abs/2003.06834
AUTHORS:        Yunfei Meng ; Zhiqiu Huang ; Senzhang Wang ; Guohua Shen ; Changbo Ke
HIGHLIGHT:      To effectively tackle the security threats towards the Internet of things, we propose a SOM-based DDoS defense mechanism using software-defined networking (SDN) in this paper.

38, TITLE:      A Quadratic Lower Bound for Algebraic Branching Programs and Formulas
http://arxiv.org/abs/1911.11793
AUTHORS:        Prerona Chatterjee ; Mrinal Kumar ; Adrian She ; Ben Lee Volk
HIGHLIGHT:      We show that any Algebraic Branching Program (ABP) computing the polynomial $\sum_{i = 1}^n x_i^n$ has at least $\Omega(n^2)$ vertices.

39, TITLE:      Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms
http://arxiv.org/abs/2002.04783
AUTHORS:        Tianyi Lin ; Nhat Ho ; Xi Chen ; Marco Cuturi ; Michael I. Jordan
COMMENTS:       Correct some typos
HIGHLIGHT:      Building on this negative result, we propose and analyze a simple and efficient variant of the iterative Bregman projection (IBP) algorithm, currently the most widely adopted algorithm to solve the FS-WBP.

40, TITLE:      Improving the Robustness of Capsule Networks to Image Affine Transformations
http://arxiv.org/abs/1911.07968
AUTHORS:        Jindong Gu ; Volker Tresp
HIGHLIGHT:      On our benchmark task where models are trained on the MNIST dataset and tested on the AffNIST dataset, our Aff-CapsNets improve the benchmark performance by a large margin (from 79% to 93.21%), without using a routing mechanism.

41, TITLE:      Finding Fair and Efficient Allocations When Valuations Don't Add Up
http://arxiv.org/abs/2003.07060
AUTHORS:        Nawal Benabbou ; Mithun Chakraborty ; Ayumi Igarashi ; Yair Zick
HIGHLIGHT:      In this paper, we present new results on the fair and efficient allocation of indivisible goods to agents that have monotone, submodular, non-additive valuation functions over bundles.

42, TITLE:      Human Action Recognition in Drone Videos using a Few Aerial Training Examples
http://arxiv.org/abs/1910.10027
AUTHORS:        Waqas Sultani ; Mubarak Shah
COMMENTS:       ECCV, 2020, under review
HIGHLIGHT:      In this paper, we explore two alternative data sources to improve aerial action classification when only a few training aerial examples are available.

43, TITLE:      Training Quantized Neural Networks with a Full-precision Auxiliary Module
http://arxiv.org/abs/1903.11236
AUTHORS:        Bohan Zhuang ; Lingqiao Liu ; Mingkui Tan ; Chunhua Shen ; Ian Reid
COMMENTS:       Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2020)
HIGHLIGHT:      In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function.

44, TITLE:      Multimodal Shape Completion via Conditional Generative Adversarial Networks
http://arxiv.org/abs/2003.07717
AUTHORS:        Rundi Wu ; Xuelin Chen ; Yixin Zhuang ; Baoquan Chen
HIGHLIGHT:      Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i.e., filling the regions that were missing in the shape. Hence, we pose a multi-modal shape completion problem, in which we seek to complete the partial shape with multiple outputs by learning a one-to-many mapping.

45, TITLE:      Deep Image Spatial Transformation for Person Image Generation
http://arxiv.org/abs/2003.00696
AUTHORS:        Yurui Ren ; Xiaoming Yu ; Junming Chen ; Thomas H. Li ; Ge Li
HIGHLIGHT:      In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level.

46, TITLE:      Make Skeleton-based Action Recognition Model Smaller, Faster and Better
http://arxiv.org/abs/1907.09658
AUTHORS:        Fan Yang ; Sakriani Sakti ; Yang Wu ; Satoshi Nakamura
COMMENTS:       6 pages, 5 figures
HIGHLIGHT:      To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based action recognition.

47, TITLE:      Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control
http://arxiv.org/abs/2003.06709
AUTHORS:        Christian Schroeder de Witt ; Bei Peng ; Pierre-Alexandre Kamienny ; Philip Torr ; Wendelin Böhmer ; Shimon Whiteson
HIGHLIGHT:      To answer this question, we propose a second new method, FacMADDPG, which factors MADDPG's critic.

48, TITLE:      Global Texture Enhancement for Fake Face Detection in the Wild
http://arxiv.org/abs/2002.00133
AUTHORS:        Zhengzhe Liu ; Xiaojuan Qi ; Philip Torr
HIGHLIGHT:      In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets.

49, TITLE:      The Group Loss for Deep Metric Learning
http://arxiv.org/abs/1912.00385
AUTHORS:        Ismail Elezi ; Sebastiano Vascon ; Alessandro Torcinovich ; Marcello Pelillo ; Laura Leal-Taixe
HIGHLIGHT:      We propose Group Loss, a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting, at the same time, low-density regions amongst data points belonging to different groups.

50, TITLE:      DebFace: De-biasing Face Recognition
http://arxiv.org/abs/1911.08080
AUTHORS:        Sixue Gong ; Xiaoming Liu ; Anil K. Jain
HIGHLIGHT:      We present a novel de-biasing adversarial network that learns to extract disentangled feature representations for both unbiased face recognition and demographics estimation.

51, TITLE:      Learning Continuous 3D Reconstructions for Geometrically Aware Grasping
http://arxiv.org/abs/1910.00983
AUTHORS:        Mark Van der Merwe ; Qingkai Lu ; Balakumar Sundaralingam ; Martin Matak ; Tucker Hermans
COMMENTS:       IEEE Conference on Robotics and Automation 2020 (ICRA 2020) Camera-Ready. Includes updated experiments from initial submission
HIGHLIGHT:      We propose to utilize a novel, learned 3D reconstruction to enable geometric awareness in a grasping system.

52, TITLE:      Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks
http://arxiv.org/abs/1910.01843
AUTHORS:        Philipp Kratzer ; Marc Toussaint ; Jim Mainprice
COMMENTS:       International Conference on Robotics and Automation (ICRA) 2020
HIGHLIGHT:      In order to alleviate this issue, we propose a prediction framework that decouples short-term prediction, linked to internal body dynamics, and long-term prediction, linked to the environment and task constraints.