2020.03.24.txt

==========New Papers==========
1, TITLE:       Robust Medical Instrument Segmentation Challenge 2019
http://arxiv.org/abs/2003.10299
AUTHORS:        Tobias Ross ; Annika Reinke ; Peter M. Full ; Martin Wagner ; Hannes Kenngott ; Martin Apitz ; Hellena Hempe ; Diana Mindroc Filimon ; Patrick Scholz ; Thuy Nuong Tran ; Pierangela Bruno ; Pablo Arbeláez ; Gui-Bin Bian ; Sebastian Bodenstedt ; Jon Lindström Bolmgren ; Laura Bravo-Sánchez ; Hua-Bin Chen ; Cristina González ; Dong Guo ; Pål Halvorsen ; Pheng-Ann Heng ; Enes Hosgor ; Zeng-Guang Hou ; Fabian Isensee ; Debesh Jha ; Tingting Jiang ; Yueming Jin ; Kadir Kirtac ; Sabrina Kletz ; Stefan Leger ; Zhixuan Li ; Klaus H. Maier-Hein ; Zhen-Liang Ni ; Michael A. Riegler ; Klaus Schoeffmann ; Ruohua Shi ; Stefanie Speidel ; Michael Stenzel ; Isabell Twick ; Gutai Wang ; Jiacheng Wang ; Liansheng Wang ; Lu Wang ; Yujie Zhang ; Yan-Jie Zhou ; Lei Zhu ; Manuel Wiesenfarth ; Annette Kopp-Schneider ; Beat P. Müller-Stich ; Lena Maier-Hein
COMMENTS:       A pre-print
HIGHLIGHT:      Robust Medical Instrument Segmentation Challenge 2019

2, TITLE:       Accurate Optimization of Weighted Nuclear Norm for Non-Rigid Structure from Motion
http://arxiv.org/abs/2003.10281
AUTHORS:        José Pedro Iglesias ; Carl Olsson ; Marcus Valtonen Örnhag
HIGHLIGHT:      In this paper we show that more accurate results can in many cases be achieved with 2nd order methods.

3, TITLE:       Adversarial Attacks on Monocular Depth Estimation
http://arxiv.org/abs/2003.10315
AUTHORS:        Ziqi Zhang ; Xinge Zhu ; Yingwei Li ; Xiangqun Chen ; Yao Guo
HIGHLIGHT:      In this paper, we present to the best of our knowledge the first systematic study of adversarial attacks on monocular depth estimation, an important task of 3D scene understanding in scenarios such as autonomous driving and robot navigation.

4, 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.

5, TITLE:       Who2com: Collaborative Perception via Learnable Handshake Communication
http://arxiv.org/abs/2003.09575
AUTHORS:        Yen-Cheng Liu ; Junjiao Tian ; Chih-Yao Ma ; Nathan Glaser ; Chia-Wen Kuo ; Zsolt Kira
COMMENTS:       Accepted to ICRA 2020
HIGHLIGHT:      In this paper, we propose the problem of collaborative perception, where robots can combine their local observations with those of neighboring agents in a learnable way to improve accuracy on a perception task. We additionally develop the AirSim-CP dataset and metrics based on the AirSim simulator where a group of aerial robots perceive diverse landscapes, such as roads, grasslands, buildings, etc.

6, TITLE:       BiCANet: Bi-directional Contextual Aggregating Network for Image Semantic Segmentation
http://arxiv.org/abs/2003.09669
AUTHORS:        Quan Zhou ; Dechun Cong ; Bin Kang ; Xiaofu Wu ; Baoyu Zheng ; Huimin Lu ; Longin Jan Latecki
HIGHLIGHT:      This paper introduces a Bi-directional Contextual Aggregating Network, called BiCANet, for semantic segmentation.

7, TITLE:       Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises
http://arxiv.org/abs/2003.09595
AUTHORS:        Bin Yan ; Dong Wang ; Huchuan Lu ; Xiaoyun Yang
COMMENTS:       CVPR2020
HIGHLIGHT:      In this paper, a cooling-shrinking attack method is proposed to deceive state-of-the-art SiameseRPN-based trackers.

8, TITLE:       Topological Sweep for Multi-Target Detection of Geostationary Space Objects
http://arxiv.org/abs/2003.09583
AUTHORS:        Daqi Liu ; Bo Chen ; Tat-Jun Chin ; Mark Rutten
COMMENTS:       12 pages, 10 figures
HIGHLIGHT:      In this paper, we propose a novel multi-target detection technique based on topological sweep, to find GEO objects from a short sequence of optical images.

9, TITLE:       A level set representation method for N-dimensional convex shape and applications
http://arxiv.org/abs/2003.09600
AUTHORS:        Lingfeng li ; Shousheng Luo ; Xue-Cheng Tai ; Jiang Yang
HIGHLIGHT:      In this work, we present a new efficient method for convex shape representation, which is regardless of the dimension of the concerned objects, using level-set approaches.

10, TITLE:      365 Dots in 2019: Quantifying Attention of News Sources
http://arxiv.org/abs/2003.09989
AUTHORS:        Alexander C. Nwala ; Michele C. Weigle ; Michael L. Nelson
COMMENTS:       This is an extended version of the paper accepted at Computation + Journalism Symposium 2020, which has been postponed because of COVID-19
HIGHLIGHT:      We investigate the overlap of topics of online news articles from a variety of sources.

11, TITLE:      Deep Reinforcement Learning with Smooth Policy
http://arxiv.org/abs/2003.09534
AUTHORS:        Qianli Shen ; Yan Li ; Haoming Jiang ; Zhaoran Wang ; Tuo Zhao
HIGHLIGHT:      In this paper, we develop a new training framework --- $\textbf{S}$mooth $\textbf{R}$egularized $\textbf{R}$einforcement $\textbf{L}$earning ($\textbf{SR}^2\textbf{L}$), where the policy is trained with smoothness-inducing regularization.

12, TITLE:      Ellipsoidal Subspace Support Vector Data Description
http://arxiv.org/abs/2003.09504
AUTHORS:        Fahad Sohrab ; Jenni Raitoharju ; Alexandros Iosifidis ; Moncef Gabbouj
HIGHLIGHT:      In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification.

13, TITLE:      Multi-Task Learning Enhanced Single Image De-Raining
http://arxiv.org/abs/2003.09689
AUTHORS:        YuLong Fan ; Rong Chen ; Bo Li
COMMENTS:       15 pages, 16 figures
HIGHLIGHT:      In this paper, we address a non-trivial issue of removing visual effect of rain streak from a single image.

14, TITLE:      Geometrically Mappable Image Features
http://arxiv.org/abs/2003.09682
AUTHORS:        Janine Thoma ; Danda Pani Paudel ; Ajad Chhatkuli ; Luc Van Gool
COMMENTS:       Implementation available at https://github.com/janinethoma/geometrically_mappable
HIGHLIGHT:      In this work, we propose a method that learns image features targeted for image-retrieval-based localization.

15, TITLE:      Cross-modal Deep Face Normals with Deactivable Skip Connections
http://arxiv.org/abs/2003.09691
AUTHORS:        Victoria Fernandez Abrevaya ; Adnane Boukhayma ; Philip H. S. Torr ; Edmond Boyer
COMMENTS:       CVPR 2020
HIGHLIGHT:      We present an approach for estimating surface normals from in-the-wild color images of faces.

16, TITLE:      Learning 3D Part Assembly from a Single Image
http://arxiv.org/abs/2003.09754
AUTHORS:        Yichen Li ; Kaichun Mo ; Lin Shao ; Minhyuk Sung ; Leonidas Guibas
HIGHLIGHT:      Towards this end, we introduce a novel problem, single-image-guided 3D part assembly, along with a learningbased solution.

17, TITLE:      Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks
http://arxiv.org/abs/2003.09717
AUTHORS:        Yang Feng ; Yu Wang ; Jiebo Luo
COMMENTS:       This work was done in 2017
HIGHLIGHT:      In this paper, we introduce a novel gating mechanism to deep neural networks.

18, TITLE:      Message complexity of population protocols
http://arxiv.org/abs/2003.09532
AUTHORS:        Talley Amir ; James Aspnes ; David Doty ; Mahsa Eftekhari H. ; Eric Severson
HIGHLIGHT:      The standard population protocol model assumes that when two agents interact, each observes the entire state of the other agent.

19, TITLE:      TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks
http://arxiv.org/abs/2003.09855
AUTHORS:        Xinyu Liu ; Xiaoguang Di
COMMENTS:       7 pages, 14 figures, submitted to IET Computer Vision
HIGHLIGHT:      In this work, we proposed a novel activation function named Tanh Exponential Activation Function (TanhExp) which can improve the performance for these networks on image classification task significantly.

20, TITLE:      HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing
http://arxiv.org/abs/2003.09876
AUTHORS:        Deyin Liu ; Xu Chen ; Zhi Zhou ; Qing Ling
COMMENTS:       Submitted for review
HIGHLIGHT:      In this paper, we propose HierTrain, a hierarchical edge AI learning framework, which efficiently deploys the DNN training task over the hierarchical MECC architecture.

21, TITLE:      CF2-Net: Coarse-to-Fine Fusion Convolutional Network for Breast Ultrasound Image Segmentation
http://arxiv.org/abs/2003.10144
AUTHORS:        Zhenyuan Ning ; Ke Wang ; Shengzhou Zhong ; Qianjin Feng ; Yu Zhang
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      To this end, we propose and evaluate a coarse-to-fine fusion convolutional network (CF2-Net) based on a novel feature integration strategy (forming an 'E'-like type) for BUS image segmentation.

22, TITLE:      Learning a Probabilistic Strategy for Computational Imaging Sensor Selection
http://arxiv.org/abs/2003.10424
AUTHORS:        He Sun ; Adrian V. Dalca ; Katherine L. Bouman
COMMENTS:       This paper has been accepted to the IEEE International Conference on Computational Photography (ICCP) 2020. Keywords: Computational Imaging, Optimized Sensing, Ising Model, Deep Learning, VLBI, Interferometry
HIGHLIGHT:      In this paper, we propose a physics-constrained, fully differentiable, autoencoder that learns a probabilistic sensor-sampling strategy for optimized sensor design.

23, TITLE:      Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation
http://arxiv.org/abs/2003.10042
AUTHORS:        Lie Ju ; Xin Wang ; Quan Zhou ; Hu Zhu ; Mehrtash Harandi ; Paul Bonnington ; Tom Drummond ; Zongyuan Ge
HIGHLIGHT:      We propose a flexible framework to bridge the domain gap between two domains and co-train a UWF fundus diagnosis model by pseudo-labelling and adversarial learning.

24, TITLE:      Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation
http://arxiv.org/abs/2003.10304
AUTHORS:        Gusztáv Gaál ; Balázs Maga ; András Lukács
COMMENTS:       7 pages, 4 figures
HIGHLIGHT:      Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline.

25, TITLE:      AQPDCITY Dataset: Picture-Based PM2.5 Monitoring in the Urban Area of Big Cities
http://arxiv.org/abs/2003.09784
AUTHORS:        Yonghui Zhang ; Ke Gu
HIGHLIGHT:      It is highly desired to devise a method that can obtain the PM concentration at any location for the following air quality control in time.

26, TITLE:      Evaluation of Parameterized Quantum Circuits: on the design, and the relation between classification accuracy, expressibility and entangling capability
http://arxiv.org/abs/2003.09887
AUTHORS:        Thomas Hubregtsen ; Josef Pichlmeier ; Koen Bertels
COMMENTS:       Pre-Print
HIGHLIGHT:      In this work, we will investigate any potential relation between the classification accuracy and two of these descriptors, being expressibility and entangling capability.

27, TITLE:      Analyzing Word Translation of Transformer Layers
http://arxiv.org/abs/2003.09586
AUTHORS:        Hongfei Xu ; Josef van Genabith ; Deyi Xiong ; Qiuhui Liu
HIGHLIGHT:      In this paper, we propose approaches to analyze the translation performed in encoder / decoder layers of the Transformer.

28, TITLE:      A Framework for Generating Explanations from Temporal Personal Health Data
http://arxiv.org/abs/2003.09530
AUTHORS:        Jonathan J. Harris ; Ching-Hua Chen ; Mohammed J. Zaki
COMMENTS:       30 pages, 11 figures
HIGHLIGHT:      We aim to bridge the gap between data collection and explanation generation by mining the data for interesting behavioral findings that may provide hints about a user's tendencies.

29, TITLE:      Prior Knowledge Driven Label Embedding for Slot Filling in Natural Language Understanding
http://arxiv.org/abs/2003.09831
AUTHORS:        Su Zhu ; Zijian Zhao ; Rao Ma ; Kai Yu
COMMENTS:       11 pages, 6 figures; Accepted for IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING
HIGHLIGHT:      To address this issue, a novel label embedding based slot filling framework is proposed in this paper.

30, TITLE:      TArC: Incrementally and Semi-Automatically Collecting a Tunisian Arabish Corpus
http://arxiv.org/abs/2003.09520
AUTHORS:        Elisa Gugliotta ; Marco Dinarelli
HIGHLIGHT:      In this article we will describe preliminary work on the TArC semi-automatic construction process and some of the first analyses we developed on TArC.

31, TITLE:      SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection
http://arxiv.org/abs/2003.09833
AUTHORS:        Xiaoya Li ; Yuxian Meng ; Qinghong Han ; Fei Wu ; Jiwei Li
HIGHLIGHT:      In this paper, we present a method for accelerating and structuring self-attentions: Sparse Adaptive Connection (SAC).

32, TITLE:      A Joint Approach to Compound Splitting and Idiomatic Compound Detection
http://arxiv.org/abs/2003.09606
AUTHORS:        Irina Krotova ; Sergey Aksenov ; Ekaterina Artemova
COMMENTS:       8 pages, 5 tables, 1 figure, accepted at LREC 2020
HIGHLIGHT:      We develop a two-fold deep learning-based approach of noun compound splitting and idiomatic compound detection for the German language that we train using a newly collected corpus of annotated German compounds.

33, TITLE:      Visual Question Answering for Cultural Heritage
http://arxiv.org/abs/2003.09853
AUTHORS:        Pietro Bongini ; Federico Becattini ; Andrew D. Bagdanov ; Alberto Del Bimbo
COMMENTS:       accepted at FlorenceHeritech 2020
HIGHLIGHT:      The advantages are twofold: on the one hand the cognitive burden of the visitor will decrease, limiting the flow of information to what the user actually wants to hear; and on the other hand it proposes the most natural way of interacting with a guide, favoring engagement.

34, TITLE:      COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images
http://arxiv.org/abs/2003.09871
AUTHORS:        Linda Wang ; Alexander Wong
COMMENTS:       6 pages
HIGHLIGHT:      Therefore, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest radiography images that is open source and available to the general public.

35, TITLE:      Ensembles of Deep Neural Networks for Action Recognition in Still Images
http://arxiv.org/abs/2003.09893
AUTHORS:        Sina Mohammadi ; Sina Ghofrani Majelan ; Shahriar B. Shokouhi
COMMENTS:       5 pages, 2 figures, 3 tables, Accepted by ICCKE 2019
HIGHLIGHT:      In this paper, by taking advantage of pre-trained CNNs, we employ the transfer learning technique to tackle the lack of massive labeled action recognition datasets.

36, TITLE:      Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification
http://arxiv.org/abs/2003.09860
AUTHORS:        Feng Shi ; Liming Xia ; Fei Shan ; Dijia Wu ; Ying Wei ; Huan Yuan ; Huiting Jiang ; Yaozong Gao ; He Sui ; Dinggang Shen
HIGHLIGHT:      In this study, a total of 1658 patients with COVID-19 and 1027 patients of CAP underwent thin-section CT.

37, TITLE:      Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval
http://arxiv.org/abs/2003.09869
AUTHORS:        Xinxun Xu ; Cheng Deng ; Muli Yang ; Hao Wang
HIGHLIGHT:      In this paper, we propose a Progressive Domain-independent Feature Decomposition (PDFD) network for ZS-SBIR.

38, TITLE:      Low Latency ASR for Simultaneous Speech Translation
http://arxiv.org/abs/2003.09891
AUTHORS:        Thai Son Nguyen ; Jan Niehues ; Eunah Cho ; Thanh-Le Ha ; Kevin Kilgour ; Markus Muller ; Matthias Sperber ; Sebastian Stueker ; Alex Waibel
HIGHLIGHT:      User studies have shown that reducing the latency of our simultaneous lecture translation system should be the most important goal.

39, TITLE:      Universal Differentiable Renderer for Implicit Neural Representations
http://arxiv.org/abs/2003.09852
AUTHORS:        Lior Yariv ; Matan Atzmon ; Yaron Lipman
HIGHLIGHT:      The goal of this work is to learn implicit 3D shape representation with 2D supervision (i.e., a collection of images).

40, TITLE:      A MEMS-based Foveating LIDAR to enable Real-time Adaptive Depth Sensing
http://arxiv.org/abs/2003.09545
AUTHORS:        Francesco Pittaluga ; Zaid Tasneem ; Justin Folden ; Brevin Tilmon ; Ayan Chakrabarti ; Sanjeev J. Koppal
COMMENTS:       17 pages, 6 figures, project site: https://www.fpittaluga.com/adaptivelidar
HIGHLIGHT:      We propose a hardware LIDAR design that allows flexible real-time measurements according to dynamically specified measurement patterns.

41, TITLE:      Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks
http://arxiv.org/abs/2003.09514
AUTHORS:        Tony C. W. Mok ; Albert C. S. Chung
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we present a novel, efficient unsupervised symmetric image registration method which maximizes the similarity between images within the space of diffeomorphic maps and estimates both forward and inverse transformations simultaneously.

42, TITLE:      Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data
http://arxiv.org/abs/2003.09572
AUTHORS:        Yuxiao Zhou ; Marc Habermann ; Weipeng Xu ; Ikhsanul Habibie ; Christian Theobalt ; Feng Xu
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy.

43, TITLE:      Appearance Fusion of Multiple Cues for Video Co-localization
http://arxiv.org/abs/2003.09556
AUTHORS:        Koteswar Rao Jerripothula
COMMENTS:       9 Pages and 9 figures. Submitted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
HIGHLIGHT:      To overcome this, in this paper, we propose a novel appearance fusion method where we fuse appearance models derived from these cues rather than spatially fusing their maps.

44, TITLE:      Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
http://arxiv.org/abs/2003.10423
AUTHORS:        Qian Long ; Zihan Zhou ; Abhibav Gupta ; Fei Fang ; Yi Wu ; Xiaolong Wang
COMMENTS:       The project page is https://sites.google.com/view/epciclr2020 .The source code is released at https://github.com/qian18long/epciclr2020
HIGHLIGHT:      In this paper, we introduce Evolutionary Population Curriculum (EPC), a curriculum learning paradigm that scales up Multi-Agent Reinforcement Learning (MARL) by progressively increasing the population of training agents in a stage-wise manner.

45, TITLE:      Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
http://arxiv.org/abs/2003.10397
AUTHORS:        Charles G. Frye ; James Simon ; Neha S. Wadia ; Andrew Ligeralde ; Michael R. DeWeese ; Kristofer E. Bouchard
COMMENTS:       18 pages, 5 figures
HIGHLIGHT:      We describe how the presence of these regions necessitates care in both interpreting past results that claimed to find critical points of neural network losses and in designing second-order methods for optimizing neural networks.

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

47, TITLE:      A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits
http://arxiv.org/abs/2003.10308
AUTHORS:        Alessandro Di Nuovo
COMMENTS:       Accepted for presentation at IJCNN 2020
HIGHLIGHT:      Developmental psychology and neuroimaging research identified a close link between numbers and fingers, which can boost the initial number knowledge in children.

48, TITLE:      Caption Generation of Robot Behaviors based on Unsupervised Learning of Action Segments
http://arxiv.org/abs/2003.10066
AUTHORS:        Koichiro Yoshino ; Kohei Wakimoto ; Yuta Nishimura ; Satoshi Nakamura
COMMENTS:       Will appear in IWSDS2020
HIGHLIGHT:      In this paper, we propose a system for generating natural language captions that describe behaviors of human assisting robots.

49, TITLE:      Towards Automatic Bayesian Optimization: A first step involving acquisition functions
http://arxiv.org/abs/2003.09643
AUTHORS:        Eduardo C. Garrido Merchán ; Luis C. Jariego Pérez
HIGHLIGHT:      In this paper, we propose a first attempt over automatic bayesian optimization by exploring several heuristics that automatically tune the acquisition function of bayesian optimization.

50, TITLE:      Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings
http://arxiv.org/abs/2003.10224
AUTHORS:        Christos Xypolopoulos ; Antoine J. -P. Tixier ; Michalis Vazirgiannis
COMMENTS:       Equal contribution by Christos Xypolopoulos and Antoine J.-P. Tixier
HIGHLIGHT:      We propose a novel method to estimate polysemy, based on simple geometry in the contextual embedding space.

51, TITLE:      Fast Cross-domain Data Augmentation through Neural Sentence Editing
http://arxiv.org/abs/2003.10254
AUTHORS:        Guillaume Raille ; Sandra Djambazovska ; Claudiu Musat
COMMENTS:       7 pages, 2 figures, 4 tables
HIGHLIGHT:      We thus aim to learn this in a source domain where data is abundant and apply it in a different, target domain, where data is scarce - cross-domain augmentation.

52, TITLE:      Basic concepts, definitions, and methods in D number theory
http://arxiv.org/abs/2003.09661
AUTHORS:        Xinyang Deng
COMMENTS:       28 pages, 2 figures
HIGHLIGHT:      In this paper, several crucial aspects in constructing a perfect and systematic framework of DNT are considered.

53, TITLE:      Imagination-Augmented Deep Learning for Goal Recognition
http://arxiv.org/abs/2003.09529
AUTHORS:        Thibault Duhamel ; Mariane Maynard ; Froduald Kabanza
HIGHLIGHT:      In this paper, we introduce a novel idea of using a symbolic planner to compute plan-cost insights, which augment a deep neural network with an imagination capability, leading to improved goal recognition accuracy in real and synthetic domains compared to a symbolic recognizer or a deep-learning goal recognizer alone.

54, TITLE:      E2EET: From Pipeline to End-to-end Entity Typing via Transformer-Based Embeddings
http://arxiv.org/abs/2003.10097
AUTHORS:        Michael Stewart ; Wei Liu
HIGHLIGHT:      In light of these drawbacks we propose to incorporate context using transformer-based embeddings for a mention-level model, and an end-to-end model using a Bi-GRU to remove the dependency on window size.

55, TITLE:      FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques
http://arxiv.org/abs/2003.09579
AUTHORS:        Tai Vu ; Leon Tran
HIGHLIGHT:      We seek to apply reinforcement learning algorithms to the game Flappy Bird.

56, TITLE:      Deep Soft Procrustes for Markerless Volumetric Sensor Alignment
http://arxiv.org/abs/2003.10176
AUTHORS:        Vladimiros Sterzentsenko ; Alexandros Doumanoglou ; Spyridon Thermos ; Nikolaos Zioulis ; Dimitrios Zarpalas ; Petros Daras
COMMENTS:       10 pages, 7 figures, to appear in IEEE VR 2020. Code and models at https://vcl3d.github.io/StructureNet/
HIGHLIGHT:      In this work, we improve markerless data-driven correspondence estimation to achieve more robust and flexible multi-sensor spatial alignment.

57, TITLE:      SOLOv2: Dynamic, Faster and Stronger
http://arxiv.org/abs/2003.10152
AUTHORS:        Xinlong Wang ; Rufeng Zhang ; Tao Kong ; Lei Li ; Chunhua Shen
COMMENTS:       12 pages
HIGHLIGHT:      In this work, we aim at building a simple, direct, and fast instance segmentation framework with strong performance.

58, TITLE:      Depth Edge Guided CNNs for Sparse Depth Upsampling
http://arxiv.org/abs/2003.10138
AUTHORS:        Yi Guo ; Ji Liu
HIGHLIGHT:      Inspired by the normalized convolution operation, we propose a guided convolutional layer to recover dense depth from sparse and irregular depth image with an depth edge image as guidance.

59, TITLE:      EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion
http://arxiv.org/abs/2003.10142
AUTHORS:        Chia-Yuan Chang ; Shuo-En Chang ; Pei-Yung Hsiao ; Li-Chen Fu
COMMENTS:       Technical report
HIGHLIGHT:      In this work, we propose an Efficient Panoptic Segmentation Network (EPSNet) to tackle the panoptic segmentation tasks with fast inference speed.

60, TITLE:      GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end
http://arxiv.org/abs/2003.10151
AUTHORS:        Ahmed Samy Nassar ; Sébastien Lefèvre ; Jan D. Wegner
HIGHLIGHT:      In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object.

61, TITLE:      Balanced Alignment for Face Recognition: A Joint Learning Approach
http://arxiv.org/abs/2003.10168
AUTHORS:        Huawei Wei ; Peng Lu ; Yichen Wei
COMMENTS:       17 pages, 9 figures
HIGHLIGHT:      To strike the balance, our second contribution is a novel joint learning approach where alignment learning is controllable with respect to its strength and driven by recognition.

62, TITLE:      RobustGCNs: Robust Norm Graph Convolutional Networks in the Presence of Node Missing Data and Large Noises
http://arxiv.org/abs/2003.10130
AUTHORS:        Bo Jiang ; Ziyan Zhang
HIGHLIGHT:      To address this issue, in this paper, we propose to incorporate robust norm feature learning mechanism into graph convolution and present Robust Graph Convolutions (RGCs) for graph data in the presence of feature noises and missing values.

63, TITLE:      Deep Sets for Generalization in RL
http://arxiv.org/abs/2003.09443
AUTHORS:        Tristan Karch ; Cédric Colas ; Laetitia Teodorescu ; Clément Moulin-Frier ; Pierre-Yves Oudeyer
COMMENTS:       15 pages, 10 figures, published as a workshop Paper at ICLR: Beyond tabula rasa in RL (BeTR-RL)
HIGHLIGHT:      This paper investigates the idea of encoding object-centered representations in the design of the reward function and policy architectures of a language-guided reinforcement learning agent.

64, TITLE:      Adversarial Robustness on In- and Out-Distribution Improves Explainability
http://arxiv.org/abs/2003.09461
AUTHORS:        Maximilian Augustin ; Alexander Meinke ; Matthias Hein
HIGHLIGHT:      In this work we propose RATIO, a training procedure for Robustness via Adversarial Training on In- and Out-distribution, which leads to robust models with reliable and robust confidence estimates on the out-distribution.

65, TITLE:      Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks
http://arxiv.org/abs/2003.09543
AUTHORS:        Seyed Mohssen Ghafari
COMMENTS:       158 pages, 20 figures, and 19 tables. This is my PhD thesis in Macquarie University, Sydney, Australia
HIGHLIGHT:      In this dissertation, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs. Then, we propose a set of context-aware trust prediction models.

66, TITLE:      The Power of a Single Qubit: Two-way Quantum Finite Automata and the Word Problem
http://arxiv.org/abs/2003.09879
AUTHORS:        Zachary Remscrim
HIGHLIGHT:      As a further corollary, we show that 2QCFA can recognize certain non-context-free languages in expected polynomial time.

67, TITLE:      Lower Bounds on the Running Time of Two-Way Quantum Finite Automata and Sublogarithmic-Space Quantum Turing Machines
http://arxiv.org/abs/2003.09877
AUTHORS:        Zachary Remscrim
HIGHLIGHT:      Far more generally, we establish a lower bound on the running time of any 2QCFA or $o(\log n)$-space QTM that recognizes any language $L$ in terms of a natural ``hardness measure" of $L$.

68, TITLE:      The Computational Complexity of Evil Hangman
http://arxiv.org/abs/2003.10000
AUTHORS:        Jérémy Barbay ; Bernardo Subercaseaux
HIGHLIGHT:      We show that a greedy strategy for Evil Hangman can perform arbitrarily far from optimal, and most importantly, that playing optimally as an Evil Hangman setter is computationally difficult.

69, TITLE:      1 x 1 Rush Hour with Fixed Blocks is PSPACE-complete
http://arxiv.org/abs/2003.09914
AUTHORS:        Josh Brunner ; Lily Chung ; Erik D. Demaine ; Dylan Hendrickson ; Adam Hesterberg ; Adam Suhl ; Avi Zeff
COMMENTS:       14 pages, 11 figures
HIGHLIGHT:      We prove that it is PSPACE-complete to decide whether a given block can reach the left edge of the board, by reduction from Nondeterministic Constraint Logic via 2-color oriented Subway Shuffle.

70, TITLE:      $P\neq NP$
http://arxiv.org/abs/2003.09791
AUTHORS:        Tianrong Lin
COMMENTS:       Submission for review, comments are welcome
HIGHLIGHT:      The main contribution of the present paper is that a series of results are obtained.

71, TITLE:      Efficient Crowd Counting via Structured Knowledge Transfer
http://arxiv.org/abs/2003.10120
AUTHORS:        Lingbo Liu ; Jiaqi Chen ; Hefeng Wu ; Tianshui Chen ; Guanbin Li ; Liang Lin
HIGHLIGHT:      To liberate these crowd counting models, we propose a novel Structured Knowledge Transfer (SKT) framework integrating two complementary transfer modules, which can generate a lightweight but still highly effective student network by fully exploiting the structured knowledge of a well-trained teacher network.

72, TITLE:      Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images
http://arxiv.org/abs/2003.10111
AUTHORS:        Kumar Abhishek ; Ghassan Hamarneh ; Mark S. Drew
COMMENTS:       10 pages, 5 figures
HIGHLIGHT:      In this work, we propose the first deep semantic segmentation framework for dermoscopic images which incorporates, along with the original RGB images, information extracted using the physics of skin illumination and imaging.

73, TITLE:      Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection
http://arxiv.org/abs/2003.10129
AUTHORS:        Suyog Jadhav ; Udbhav Bamba ; Arnav Chavan ; Rishabh Tiwari ; Aryan Raj
COMMENTS:       EndoCV2020 workshop ISBI 2020 camera ready
HIGHLIGHT:      For Semantic segmentation task, we propose a multi-plateau ensemble of FPN (Feature Pyramid Network) with EfficientNet as feature extractor/encoder.

74, TITLE:      NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels
http://arxiv.org/abs/2003.09660
AUTHORS:        Yang Hao ; Wenbiao Ding ; Zitao Liu
HIGHLIGHT:      In this paper, we propose \emph{NeuCrowd}, a unified framework for representation learning from crowdsourced labels.

75, TITLE:      On Information Plane Analyses of Neural Network Classifiers -- A Review
http://arxiv.org/abs/2003.09671
AUTHORS:        Bernhard C. Geiger
COMMENTS:       8 pages; under review
HIGHLIGHT:      We review this evidence together with a detailed analysis how the respective information quantities were estimated.

76, TITLE:      Deep Euler method: solving ODEs by approximating the local truncation error of the Euler method
http://arxiv.org/abs/2003.09573
AUTHORS:        Xing Shen ; Xiaoliang Cheng ; Kewei Liang
HIGHLIGHT:      In this paper, we propose a deep learning-based method, deep Euler method (DEM) to solve ordinary differential equations.

77, TITLE:      Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction
http://arxiv.org/abs/2003.09868
AUTHORS:        Simon James Fong ; Gloria Li ; Nilanjan Dey ; Ruben Gonzalez Crespo ; Enrique Herrera-Viedma
COMMENTS:       19 pages
HIGHLIGHT:      In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented.

78, TITLE:      Optimization of Operation Startegy for Primary Torque based hydrostatic Drivetrain using Artificial Intelligence
http://arxiv.org/abs/2003.10011
AUTHORS:        Yusheng Xiang ; Marcus Geimer
COMMENTS:       9 pages, 23 figures
HIGHLIGHT:      Alternatively, we use deep learning algorithms to improve machines' regeneration performance.

79, TITLE:      Adaptive Informative Path Planning with Multimodal Sensing
http://arxiv.org/abs/2003.09746
AUTHORS:        Shushman Choudhury ; Nate Gruver ; Mykel J. Kochenderfer
COMMENTS:       First two authors contributed equally; International Conference on Automated Planning and Scheduling (ICAPS) 2020
HIGHLIGHT:      We evaluate our method on two domains: a simulated search-and-rescue scenario and a challenging extension to the classic RockSample problem.

80, TITLE:      Interpretable machine learning models: a physics-based view
http://arxiv.org/abs/2003.10025
AUTHORS:        Ion Matei ; Johan de Kleer ; Christoforos Somarakis ; Rahul Rai ; John S. Baras
HIGHLIGHT:      We describe how we can build models out of the p-H constructs and how we can train them.

81, TITLE:      Large-scale Ontological Reasoning via Datalog
http://arxiv.org/abs/2003.09698
AUTHORS:        Mario Alviano ; Marco Manna
COMMENTS:       15 pages, 2 tables, 1 figure, 2 algorithms, under review for the book Studies on the Semantic Web Series
HIGHLIGHT:      This paper surveys some of these compilations, and in particular the one addressing queries over Horn-$\mathcal{SHIQ}$ knowledge bases and its implementation in DLV2 enanched by a new version of the Magic Sets algorithm.

82, TITLE:      Generalized Nested Rollout Policy Adaptation
http://arxiv.org/abs/2003.10024
AUTHORS:        Tristan Cazenave
HIGHLIGHT:      In this paper we propose to generalize NRPA with a temperature and a bias and to analyze theoretically the algorithms.

83, TITLE:      Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning
http://arxiv.org/abs/2003.09799
AUTHORS:        Jiamiao Xu ; Fangzhao Wang ; Qinmu Peng ; Xinge You ; Shuo Wang ; Xiao-Yuan Jing ; C. L. Philip Chen
COMMENTS:       This article has been accepted by IEEE Transactions on Neural Networks and Learning Systems
HIGHLIGHT:      To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix.

84, TITLE:      Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction
http://arxiv.org/abs/2003.09785
AUTHORS:        Ziyi Zhao ; Zhao Jin ; Wentian Bai ; Wentan Bai ; Carlos Caicedo ; M. Cenk Gursoy ; Qinru Qiu
HIGHLIGHT:      In this paper, a deep learning-based UAS instantaneous density prediction model is presented.

85, TITLE:      Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology
http://arxiv.org/abs/2003.09802
AUTHORS:        Xinyu Liu ; Xiren Miao ; Hao Jiang ; Jing Chen
HIGHLIGHT:      With the aim of providing a comprehensive overview for researchers who are interested in developing a deep-learning-based analysis system for power lines inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future research.

86, TITLE:      Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection
http://arxiv.org/abs/2003.09790
AUTHORS:        Zhonghua Wu ; Qingyi Tao ; Guosheng Lin ; Jianfei Cai
HIGHLIGHT:      Within our approach, we introduce a bottom-up mechanism based on the well-trained fully supervised object detector (i.e. Faster RCNN) as an object region estimator for web images by recognizing the common objectiveness shared by base and novel classes.

87, TITLE:      HDF: Hybrid Deep Features for Scene Image Representation
http://arxiv.org/abs/2003.09773
AUTHORS:        Chiranjibi Sitaula ; Yong Xiang ; Anish Basnet ; Sunil Aryal ; Xuequan Lu
COMMENTS:       8 pages, Accepted in IEEE WCCI 2020 Conference
HIGHLIGHT:      In this paper, we propose a novel type of features -- hybrid deep features, for scene images.

88, TITLE:      Lifespan Age Transformation Synthesis
http://arxiv.org/abs/2003.09764
AUTHORS:        Roy Or-El ; Soumyadip Sengupta ; Ohad Fried ; Eli Shechtman ; Ira Kemelmacher-Shlizerman
HIGHLIGHT:      We propose a novel multi-domain image-to-image generative adversarial network architecture, whose learned latent space models a continuous bi-directional aging process.

89, TITLE:      Monocular Depth Prediction Through Continuous 3D Loss
http://arxiv.org/abs/2003.09763
AUTHORS:        Minghan Zhu ; Maani Ghaffari ; Yuanxin Zhong ; Pingping Lu ; Zhong Cao ; Ryan M. Eustice ; Huei Peng
COMMENTS:       8 pages, 5 figures
HIGHLIGHT:      This paper reports a new continuous 3D loss function for learning depth from monocular images.

90, TITLE:      Translation of Array-Based Loops to Distributed Data-Parallel Programs
http://arxiv.org/abs/2003.09769
AUTHORS:        Leonidas Fegaras ; Md Hasanuzzaman Noor
COMMENTS:       This is the extended version of a paper that will appear at VLDB 2020 (PVLDB Vol. 13)
HIGHLIGHT:      We present a novel framework for translating programs expressed as array-based loops to distributed data parallel programs that is more general and efficient than related work.

91, TITLE:      Understanding the robustness of deep neural network classifiers for breast cancer screening
http://arxiv.org/abs/2003.10041
AUTHORS:        Witold Oleszkiewicz ; Taro Makino ; Stanisław Jastrzębski ; Tomasz Trzciński ; Linda Moy ; Kyunghyun Cho ; Laura Heacock ; Krzysztof J. Geras
COMMENTS:       Accepted as a workshop paper at AI4AH, ICLR 2020
HIGHLIGHT:      Since low-pass filtering removes semantically meaningful information that is predictive of breast cancer, we argue that it is undesirable for mammogram image classifiers to be invariant to it.

92, TITLE:      Safe Crossover of Neural Networks Through Neuron Alignment
http://arxiv.org/abs/2003.10306
AUTHORS:        Thomas Uriot ; Dario Izzo
COMMENTS:       To appear in GECCO 2020 Proceedings
HIGHLIGHT:      In this paper, wepropose a two-stepsafe crossover(SC) operator.

93, TITLE:      Information-Theoretic Free Energy as Emotion Potential: Emotional Valence as a Function of Complexity and Novelty
http://arxiv.org/abs/2003.10073
AUTHORS:        Hideyoshi Yanagisawa
HIGHLIGHT:      This study extends the mathematical model of emotion dimensions that we previously proposed (Yanagisawa, et al. 2019, Front Comput Neurosci) to consider perceived complexity as well as novelty, as a source of arousal potential.

94, TITLE:      Deep Unfolding Network for Image Super-Resolution
http://arxiv.org/abs/2003.10428
AUTHORS:        Kai Zhang ; Luc Van Gool ; Radu Timofte
COMMENTS:       Accepted by CVPR 2020. Project page: https://github.com/cszn/USRNet
HIGHLIGHT:      To address this issue, this paper proposes an end-to-end trainable unfolding network which leverages both learning-based methods and model-based methods.

95, TITLE:      What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization
http://arxiv.org/abs/2003.10412
AUTHORS:        Caleb Belth ; Xinyi Zheng ; Jilles Vreeken ; Danai Koutra
COMMENTS:       10 pages, plus 2 pages of references. 5 figures. Accepted at The Web Conference 2020
HIGHLIGHT:      In this work, we introduce a unified solution to KG characterization by formulating the problem as unsupervised KG summarization with a set of inductive, soft rules, which describe what is normal in a KG, and thus can be used to identify what is abnormal, whether it be strange or missing.

96, TITLE:      Optimising Game Tactics for Football
http://arxiv.org/abs/2003.10294
AUTHORS:        Ryan Beal ; Georgios Chalkiadakis ; Timothy J. Norman ; Sarvapali D. Ramchurn
COMMENTS:       AAMAS 2020 Pre-Print Version
HIGHLIGHT:      In this paper we present a novel approach to optimise tactical and strategic decision making in football (soccer).

97, TITLE:      Weighting NTBEA for Game AI Optimisation
http://arxiv.org/abs/2003.10378
AUTHORS:        James Goodman ; Simon Lucas
HIGHLIGHT:      We introduce weighting functions to the model to obtain Weighted- NTBEA and test this on four benchmark functions and two game environments.

98, TITLE:      Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives
http://arxiv.org/abs/2003.09934
AUTHORS:        Jingwei Song ; Shaobo Xia ; Jun Wang ; Dong Chen
COMMENTS:       12 pages. 14 figures
HIGHLIGHT:      To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives.

99, TITLE:      Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images
http://arxiv.org/abs/2003.10033
AUTHORS:        Sharib Ali ; Binod Bhattaria ; Tae-Kyun Kim ; Jens Rittscher
COMMENTS:       10 pages
HIGHLIGHT:      In this work, we propose to use a few-shot learning approach that requires less training data and can be used to predict label classes of test samples from an unseen dataset.

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

101, TITLE:     The Instantaneous Accuracy: a Novel Metric for the Problem of Online Human Behaviour Recognition in Untrimmed Videos
http://arxiv.org/abs/2003.09970
AUTHORS:        Marcos Baptista Rios ; Roberto J. Lopez-Sastre ; Fabian Caba Heilbron ; Jan van Gemert
COMMENTS:       Published at ICCV workshop 2019: Human Behaviour Understanding
HIGHLIGHT:      In this paper we introduce a novel online metric, the Instantaneous Accuracy ($IA$), that exhibits an \emph{online} nature, solving most of the limitations of the previous (offline) metrics.

102, TITLE:     Self-Supervised 2D Image to 3D Shape Translation with Disentangled Representations
http://arxiv.org/abs/2003.10016
AUTHORS:        Berk Kaya ; Radu Timofte
HIGHLIGHT:      In this paper, we propose SIST, a Self-supervised Image to Shape Translation framework that fulfills three tasks: (i) reconstructing the 3D shape from a single image; (ii) learning disentangled representations for shape, appearance and viewpoint; and (iii) generating a realistic RGB image from these independent factors.

103, TITLE:     Dynamic ReLU
http://arxiv.org/abs/2003.10027
AUTHORS:        Yinpeng Chen ; Xiyang Dai ; Mengchen Liu ; Dongdong Chen ; Lu Yuan ; Zicheng Liu
COMMENTS:       Technical report
HIGHLIGHT:      In this paper, we propose Dynamic ReLU (DY-ReLU), a dynamic rectifier whose parameters are input-dependent as a hyper function over all input elements.

104, TITLE:     A Better Variant of Self-Critical Sequence Training
http://arxiv.org/abs/2003.09971
AUTHORS:        Ruotian Luo
HIGHLIGHT:      In this work, we present a simple yet better variant of Self-Critical Sequence Training.

105, TITLE:     Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments
http://arxiv.org/abs/2003.10249
AUTHORS:        Mohammad Etemad ; Nader Zare ; Mahtab Sarvmaili ; Amilcar Soares ; Bruno Brandoli Machado ; Stan Matwin
HIGHLIGHT:      In this work, we used deep reinforcement learning combining Q-learning with a neural representation to avoid instability.

106, TITLE:     Graph Neural Networks for Decentralized Controllers
http://arxiv.org/abs/2003.10280
AUTHORS:        Fernando Gama ; Ekaterina Tolstaya ; Alejandro Ribeiro
HIGHLIGHT:      In this paper, we use graph neural networks (GNNs) to learn decentralized controllers from data.

107, TITLE:     Invariant Rationalization
http://arxiv.org/abs/2003.09772
AUTHORS:        Shiyu Chang ; Yang Zhang ; Mo Yu ; Tommi S. Jaakkola
COMMENTS:       10 pages
HIGHLIGHT:      Instead, we introduce a game-theoretic invariant rationalization criterion where the rationales are constrained to enable the same predictor to be optimal across different environments.

108, TITLE:     PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
http://arxiv.org/abs/2003.09696
AUTHORS:        Adarsha Balaji ; Prathyusha Adiraju ; Hirak J. Kashyap ; Anup Das ; Jeffrey L. Krichmar ; Nikil D. Dutt ; Francky Catthoor
COMMENTS:       10 pages, 25 figures. Accepted for publication at International Joint Conference on Neural Networks (IJCNN) 2020
HIGHLIGHT:      We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN).

109, TITLE:     Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation
http://arxiv.org/abs/2003.10026
AUTHORS:        Ashwin Sanjay Lele ; Yan Fang ; Justin Ting ; Arijit Raychowdhury
COMMENTS:       5 pages, 7 figures, to be published in proceeding of IEEE AICAS
HIGHLIGHT:      In this paper, we propose a reinforcement based stochastic weight update technique for training a spiking CPG.

110, TITLE:     Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework
http://arxiv.org/abs/2003.09594
AUTHORS:        Mehdi Neshat ; Bradley Alexander ; Nataliia Y. Sergiienko ; Markus Wagner
HIGHLIGHT:      The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fully-submerged three-tether wave energy converters (WECs).

111, TITLE:     Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
http://arxiv.org/abs/2003.09917
AUTHORS:        Weiyu Chen ; Hisao Ishibuchi ; Ke Shang
COMMENTS:       2019 IEEE Symposium Series on Computational Intelligence (SSCI)
HIGHLIGHT:      In this paper, we examine the effects of the decision space discretization, objective space discretization and simultaneous discretization on the performance of NSGA-II through computational experiments on the DTLZ and WFG problems.

112, TITLE:     An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems
http://arxiv.org/abs/2003.09847
AUTHORS:        Khanh N. Dang ; Abderazek Ben Abdallah
HIGHLIGHT:      This work presents an efficient software-hardware design framework for developing SNN systems in hardware.

113, TITLE:     Non-Adversarial Video Synthesis with Learned Priors
http://arxiv.org/abs/2003.09565
AUTHORS:        Abhishek Aich ; Akash Gupta ; Rameswar Panda ; Rakib Hyder ; M. Salman Asif ; Amit K. Roy-Chowdhury
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames.

114, TITLE:     Single-shot autofocusing of microscopy images using deep learning
http://arxiv.org/abs/2003.09585
AUTHORS:        Yilin Luo ; Luzhe Huang ; Yair Rivenson ; Aydogan Ozcan
COMMENTS:       34 pages, 8 figures, 1 table
HIGHLIGHT:      We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that is acquired at an arbitrary out-of-focus plane.

115, TITLE:     Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning
http://arxiv.org/abs/2003.10063
AUTHORS:        Thiago M. Paixao ; Rodrigo F. Berriel ; Maria C. S. Boeres ; Alessando L. Koerich ; Claudine Badue ; Alberto F. De Souza ; Thiago Oliveira-Santos
COMMENTS:       Accepted for CVPR 2020. 9 pages, 10 figures
HIGHLIGHT:      This work proposes a scalable deep learning approach for measuring pairwise compatibility in which the number of inferences scales linearly (rather than quadratically) with the number of shreds.

116, TITLE:     Linguistically Driven Graph Capsule Network for Visual Question Reasoning
http://arxiv.org/abs/2003.10065
AUTHORS:        Qingxing Cao ; Xiaodan Liang ; Keze Wang ; Liang Lin
COMMENTS:       Submitted to TPAMI 2020. We have achieved an end-to-end interpretable structural reasoning for general images without the requirement of layout annotations
HIGHLIGHT:      Inspired by the property of a capsule network that can carve a tree structure inside a regular convolutional neural network (CNN), we propose a hierarchical compositional reasoning model called the "Linguistically driven Graph Capsule Network", where the compositional process is guided by the linguistic parse tree.

117, TITLE:     ASLFeat: Learning Local Features of Accurate Shape and Localization
http://arxiv.org/abs/2003.10071
AUTHORS:        Zixin Luo ; Lei Zhou ; Xuyang Bai ; Hongkai Chen ; Jiahui Zhang ; Yao Yao ; Shiwei Li ; Tian Fang ; Long Quan
COMMENTS:       Accepted to CVPR 2020, supplementary materials included, code available
HIGHLIGHT:      In this paper, we present ASLFeat, with three light-weight yet effective modifications to mitigate above issues.

118, TITLE:     Architectural Resilience to Foreground-and-Background Adversarial Noise
http://arxiv.org/abs/2003.10045
AUTHORS:        Carl Cheng ; Evan Hu
COMMENTS:       9 pages, 8 figures
HIGHLIGHT:      In this work, we instead propose distinct model-agnostic benchmark perturbations of images in order to investigate the resilience and robustness of different network architectures.

119, TITLE:     Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles
http://arxiv.org/abs/2003.09881
AUTHORS:        Malte Ostendorff ; Terry Ruas ; Moritz Schubotz ; Georg Rehm ; Bela Gipp
COMMENTS:       Accepted at ACM/IEEE Joint Conference on Digital Libraries (JCDL 2020)
HIGHLIGHT:      In this paper, we model the problem of finding the relationship between two documents as a pairwise document classification task.

120, TITLE:     Learning Dynamic Routing for Semantic Segmentation
http://arxiv.org/abs/2003.10401
AUTHORS:        Yanwei Li ; Lin Song ; Yukang Chen ; Zeming Li ; Xiangyu Zhang ; Xingang Wang ; Jian Sun
COMMENTS:       Accepted by CVPR 2020 as Oral
HIGHLIGHT:      To demonstrate the superiority of the dynamic property, we compare with several static architectures, which can be modeled as special cases in the routing space.

121, TITLE:     Atlas: End-to-End 3D Scene Reconstruction from Posed Images
http://arxiv.org/abs/2003.10432
AUTHORS:        Zak Murez ; Tarrence van As ; James Bartolozzi ; Ayan Sinha ; Vijay Badrinarayanan ; Andrew Rabinovich
HIGHLIGHT:      We present an end-to-end 3D reconstruction method for a scene by directly regressing a truncated signed distance function (TSDF) from a set of posed RGB images.

122, TITLE:     Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations
http://arxiv.org/abs/2003.10399
AUTHORS:        Saima Sharmin ; Nitin Rathi ; Priyadarshini Panda ; Kaushik Roy
HIGHLIGHT:      In this work, we demonstrate that accuracy degradation is less severe in SNNs than in their non-spiking counterparts for CIFAR10 and CIFAR100 datasets on deep VGG architectures.

123, TITLE:     Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows
http://arxiv.org/abs/2003.10350
AUTHORS:        Andrei Zanfir ; Eduard Gabriel Bazavan ; Hongyi Xu ; Bill Freeman ; Rahul Sukthankar ; Cristian Sminchisescu
HIGHLIGHT:      In this paper we present practical semi-supervised and self-supervised models that support training and good generalization in real-world images and video.

124, TITLE:     Sample-Specific Output Constraints for Neural Networks
http://arxiv.org/abs/2003.10258
AUTHORS:        Mathis Brosowsky ; Olaf Dünkel ; Daniel Slieter ; Marius Zöllner
HIGHLIGHT:      We address this and propose ConstraintNet, a neural network with the capability to constrain the output space in each forward pass via an additional input.

125, TITLE:     Spatial Pyramid Based Graph Reasoning for Semantic Segmentation
http://arxiv.org/abs/2003.10211
AUTHORS:        Xia Li ; Yibo Yang ; Qijie Zhao ; Tiancheng Shen ; Zhouchen Lin ; Hong Liu
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we apply graph convolution into the semantic segmentation task and propose an improved Laplacian.

126, TITLE:     Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation
http://arxiv.org/abs/2003.10275
AUTHORS:        Yangtao Zheng ; Di Huang ; Songtao Liu ; Yunhong Wang
HIGHLIGHT:      To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection.

127, TITLE:     Learning Better Lossless Compression Using Lossy Compression
http://arxiv.org/abs/2003.10184
AUTHORS:        Fabian Mentzer ; Luc Van Gool ; Michael Tschannen
COMMENTS:       CVPR'20 camera-ready version
HIGHLIGHT:      We leverage the powerful lossy image compression algorithm BPG to build a lossless image compression system.

128, TITLE:     A Robotic 3D Perception System for Operating Room Environment Awareness
http://arxiv.org/abs/2003.09487
AUTHORS:        Zhaoshuo Li ; Amirreza Shaban ; Jean-Gabriel Simard ; Dinesh Rabindran ; Simon DiMaio ; Omid Mohareri
COMMENTS:       Accepted in IPCAI 2020
HIGHLIGHT:      Purpose: We describe a 3D multi-view perception system for the da Vinci surgical system to enable Operating room (OR) scene understanding and context awareness.

129, TITLE:     Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation?
http://arxiv.org/abs/2003.09483
AUTHORS:        Jie Luo ; Guangshen Ma ; Sarah Frisken ; Parikshit Juvekar ; Nazim Haouchine ; Zhe Xu ; Yiming Xiao ; Alexandra Golby ; Patrick Codd ; Masashi Sugiyama ; William Wells III
COMMENTS:       Draft 1
HIGHLIGHT:      In this study, we use the variogram to screen the manually annotated landmarks in two datasets used to benchmark registration in image-guided neurosurgeries.

130, TITLE:     ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
http://arxiv.org/abs/2003.09439
AUTHORS:        Tariq Bdair ; Nassir Navab ; Shadi Albarqouni
HIGHLIGHT:      In this paper, we argue that this option is limited, instead, we propose ROAM, a random layer mixup, which encourages the network to be less confident for interpolated data points at randomly selected space.

131, TITLE:     On Interactive Machine Learning and the Potential of Cognitive Feedback
http://arxiv.org/abs/2003.10365
AUTHORS:        Chris J. Michael ; Dina Acklin ; Jaelle Scheuerman
COMMENTS:       14 pages, 2 figures, submitted and accepted to the 2nd Workshop on Deep Models and Artificial Intelligence for Defense Applications: Potentials, Theories, Practices, Tools and Risks sponsored by the Association for the Advancement of Artificial Intelligence in cooperation with the Stanford University Computer Science Department
HIGHLIGHT:      In this paper, we introduce interactive machine learning and explain its advantages and limitations within the context of defense applications.

132, TITLE:     Understanding the Power and Limitations of Teaching with Imperfect Knowledge
http://arxiv.org/abs/2003.09712
AUTHORS:        Rati Devidze ; Farnam Mansouri ; Luis Haug ; Yuxin Chen ; Adish Singla
HIGHLIGHT:      Inspired by real-world applications of machine teaching in education, we consider the setting where teacher's knowledge is limited and noisy, and the key research question we study is the following: When does a teacher succeed or fail in effectively teaching a learner using its imperfect knowledge?

133, TITLE:     One-Shot Informed Robotic Visual Search in the Wild
http://arxiv.org/abs/2003.10010
AUTHORS:        Karim Koreitem ; Florian Shkurti ; Travis Manderson ; Wei-Di Chang ; Juan Camilo Gamboa Higuera ; Gregory Dudek
HIGHLIGHT:      In this paper we propose a method that enables informed visual navigation via a learned visual similarity operator that guides the robot's visual search towards parts of the scene that look like an exemplar image, which is given by the user as a high-level specification for data collection.



==========Updates to Previous Papers==========
1, TITLE:       Towards Visually Explaining Variational Autoencoders
http://arxiv.org/abs/1911.07389
AUTHORS:        Wenqian Liu ; Runze Li ; Meng Zheng ; Srikrishna Karanam ; Ziyan Wu ; Bir Bhanu ; Richard J. Radke ; Octavia Camps
COMMENTS:       10 pages, 8 figures, 3 tables, CVPR 2020
HIGHLIGHT:      In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention.

2, TITLE:       Unsupervised Sketch-to-Photo Synthesis
http://arxiv.org/abs/1909.08313
AUTHORS:        Runtao Liu ; Qian Yu ; Stella Yu
COMMENTS:       23 pages, 15 figures. URL: https://github.com/rt219/Unpaired-Sketch-to-Photo-Translation
HIGHLIGHT:      Our key insight is to decompose unsupervised sketch-to-photo synthesis into a two-stage translation task: First shape translation from sketches to grayscale photos and then content enrichment from grayscale to color photos.

3, 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.

4, TITLE:       Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
http://arxiv.org/abs/1912.07372
AUTHORS:        Michael Niemeyer ; Lars Mescheder ; Michael Oechsle ; Andreas Geiger
HIGHLIGHT:      In this work, we propose a differentiable rendering formulation for implicit shape and texture representations.

5, TITLE:       Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (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.

6, TITLE:       LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local 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.

7, TITLE:       MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning
http://arxiv.org/abs/2001.06902
AUTHORS:        Simon Vandenhende ; Stamatios Georgoulis ; Luc Van Gool
COMMENTS:       Updated version of the paper
HIGHLIGHT:      In this paper, we argue about the importance of considering task interactions at multiple scales when distilling task information in a multi-task learning setup.

8, TITLE:       DoveNet: Deep Image Harmonization via Domain Verification
http://arxiv.org/abs/1911.13239
AUTHORS:        Wenyan Cong ; Jianfu Zhang ; Li Niu ; Liu Liu ; Zhixin Ling ; Weiyuan Li ; Liqing Zhang
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this work, we contribute an image harmonization dataset iHarmony4 by generating synthesized composite images based on COCO (resp., Adobe5k, Flickr, day2night) dataset, leading to our HCOCO (resp., HAdobe5k, HFlickr, Hday2night) sub-dataset.

9, TITLE:       AQPDBJUT Dataset: Picture-Based PM Monitoring in the Campus of BJUT
http://arxiv.org/abs/2003.08609
AUTHORS:        Yonghui Zhang ; Ke Gu
HIGHLIGHT:      As the source of PM prevention and control, developing a good model for PM monitoring is extremely urgent and has posed a big challenge.

10, TITLE:      Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions
http://arxiv.org/abs/1905.10700
AUTHORS:        Ziang Xiao ; Michelle X. Zhou ; Q. Vera Liao ; Gloria Mark ; Changyan Chi ; Wenxi Chen ; Huahai Yang
COMMENTS:       The paper is accepted by ACM Transactions on Computer-Human Interaction (TOCHI)
HIGHLIGHT:      In this study with mostly open-ended questions, half of the participants took a typical online survey on Qualtrics and the other half interacted with an AI-powered chatbot to complete a conversational survey.

11, TITLE:      PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction
http://arxiv.org/abs/1902.01453
AUTHORS:        Johan Mathe ; Nina Miolane ; Nicolas Sebastien ; Jeremie Lequeux
COMMENTS:       8 pages
HIGHLIGHT:      In this paper, we introduce a Long-Term Recurrent Convolutional Network using Numerical Weather Predictions (NWP) to predict, in turn, PV production in the 24-hour and 48-hour forecast horizons.

12, TITLE:      Active Learning in Video Tracking
http://arxiv.org/abs/1912.12557
AUTHORS:        Sima Behpour
HIGHLIGHT:      We propose an adversarial approach for active learning with structured prediction domains that is tractable for matching.

13, TITLE:      Privacy, Altruism, and Experience: Estimating the Perceived Value of Internet Data for Medical Uses
http://arxiv.org/abs/1906.08562
AUTHORS:        Gilie Gefen ; Omer Ben-Porat ; Moshe Tennenholtz ; Elad Yom-Tov
HIGHLIGHT:      Here we describe experiments where methods from Mechanism Design were used to elicit a truthful valuation from users for their Internet data and for services to screen people for medical conditions.

14, TITLE:      In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction from 2D Landmarks
http://arxiv.org/abs/1911.11924
AUTHORS:        Heng Yang ; Luca Carlone
COMMENTS:       Camera-ready, CVPR 2020. 18 pages, 5 figures, 1 table
HIGHLIGHT:      We study the problem of 3D shape reconstruction from 2D landmarks extracted in a single image.

15, TITLE:      Tangent Images for Mitigating Spherical Distortion
http://arxiv.org/abs/1912.09390
AUTHORS:        Marc Eder ; Mykhailo Shvets ; John Lim ; Jan-Michael Frahm
COMMENTS:       Accepted to CVPR 2020 (8 pages, 12 pages supplementary). Code: https://github.com/meder411/Tangent-Images
HIGHLIGHT:      In this work, we propose "tangent images," a spherical image representation that facilitates transferable and scalable $360^\circ$ computer vision.

16, TITLE:      GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling
http://arxiv.org/abs/2003.06788
AUTHORS:        Yahui Liu ; Marco De Nadai ; Jian Yao ; Nicu Sebe ; Bruno Lepri ; Xavier Alameda-Pineda
COMMENTS:       27 pages, 17 figures
HIGHLIGHT:      To overcome these limitations, we propose a method named GMM-UNIT, which is based on a content-attribute disentangled representation where the attribute space is fitted with a GMM.

17, TITLE:      Gate-Shift Networks for Video Action Recognition
http://arxiv.org/abs/1912.00381
AUTHORS:        Swathikiran Sudhakaran ; Sergio Escalera ; Oswald Lanz
COMMENTS:       CVPR20 camera ready version. Code and models available at https://github.com/swathikirans/GSM
HIGHLIGHT:      In this paper we introduce spatial gating in spatial-temporal decomposition of 3D kernels.

18, TITLE:      AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs
http://arxiv.org/abs/2001.03535
AUTHORS:        Pengfei Xu ; Xiaofan Zhang ; Cong Hao ; Yang Zhao ; Yongan Zhang ; Yue Wang ; Chaojian Li ; Zetong Guan ; Deming Chen ; Yingyan Lin
COMMENTS:       Accepted by 28th ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA'2020)
HIGHLIGHT:      To enable fast and effective DNN chip design, we propose AutoDNNchip - a DNN chip generator that can automatically generate both FPGA- and ASIC-based DNN chip implementation given DNNs from machine learning frameworks (e.g., PyTorch) for a designated application and dataset.

19, TITLE:      Out-of-domain Detection for Natural Language Understanding in Dialog Systems
http://arxiv.org/abs/1909.03862
AUTHORS:        Yinhe Zheng ; Guanyi Chen ; Minlie Huang
COMMENTS:       Accepted by TALSP
HIGHLIGHT:      In this paper, we propose a novel model to generate high-quality pseudo OOD samples that are akin to IN-Domain (IND) input utterances, and thereby improves the performance of OOD detection.

20, 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.

21, TITLE:      Automatic Creation of Text Corpora for Low-Resource Languages from the Internet: The Case of Swiss German
http://arxiv.org/abs/1912.00159
AUTHORS:        Lucy Linder ; Michael Jungo ; Jean Hennebert ; Claudiu Musat ; Andreas Fischer
COMMENTS:       Submitted to LREC 2020
HIGHLIGHT:      This paper presents SwissCrawl, the largest Swiss German text corpus to date.

22, TITLE:      Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
http://arxiv.org/abs/2001.06826
AUTHORS:        Chunle Guo ; Chongyi Li ; Jichang Guo ; Chen Change Loy ; Junhui Hou ; Sam Kwong ; Runmin Cong
COMMENTS:       10 pages
HIGHLIGHT:      The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.

23, 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.

24, TITLE:      Deep learning with noisy labels: exploring techniques and remedies in medical image analysis
http://arxiv.org/abs/1912.02911
AUTHORS:        Davood Karimi ; Haoran Dou ; Simon K. Warfield ; Ali Gholipour
HIGHLIGHT:      Our review shows that recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image analysis community.

25, TITLE:      Attention Guided Anomaly Localization in Images
http://arxiv.org/abs/1911.08616
AUTHORS:        Shashanka Venkataramanan ; Kuan-Chuan Peng ; Rajat Vikram Singh ; Abhijit Mahalanobis
COMMENTS:       technical report; 19 pages, 8 tables, 5 figures
HIGHLIGHT:      Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information.

26, TITLE:      Self-Supervised Discovering of Causal Features: Towards Interpretable Reinforcement Learning
http://arxiv.org/abs/2003.07069
AUTHORS:        Wenjie Shi ; Shiji Song ; Zhuoyuan Wang ; Gao Huang
HIGHLIGHT:      In this paper, we propose a self-supervised interpretable framework, which employs a self-supervised interpretable network (SSINet) to discover and locate fine-grained causal features that constitute most evidence for the agent's decisions.

27, TITLE:      KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
http://arxiv.org/abs/2002.12687
AUTHORS:        Yang You ; Yujing Lou ; Chengkun Li ; Zhoujun Cheng ; Liangwei Li ; Lizhuang Ma ; Cewu Lu ; Weiming Wang
COMMENTS:       8 pages; to appear in CVPR 2020
HIGHLIGHT:      To handle the inconsistency between annotations from different people, we propose a novel method to aggregate these keypoints automatically, through minimization of a fidelity loss.

28, TITLE:      Foldover Features for Dynamic Object Behavior Description in Microscopic Videos
http://arxiv.org/abs/2003.08628
AUTHORS:        Xialin Li ; Chen Li ; Wenwei Zhao
HIGHLIGHT:      To this end, we propose foldover features to describe the behavior of dynamic objects.

29, TITLE:      Cautious Reinforcement Learning with Logical Constraints
http://arxiv.org/abs/2002.12156
AUTHORS:        Mohammadhosein Hasanbeig ; Alessandro Abate ; Daniel Kroening
COMMENTS:       Accepted to AAMAS 2020. arXiv admin note: text overlap with arXiv:1902.00778
HIGHLIGHT:      This paper presents the concept of an adaptive safe padding that forces Reinforcement Learning (RL) to synthesise optimal control policies while ensuring safety during the learning process.

30, TITLE:      Online Replanning in Belief Space for Partially Observable Task and Motion Problems
http://arxiv.org/abs/1911.04577
AUTHORS:        Caelan Reed Garrett ; Chris Paxton ; Tomás Lozano-Pérez ; Leslie Pack Kaelbling ; Dieter Fox
COMMENTS:       IEEE International Conference on Robotics and Automation (ICRA), 2020
HIGHLIGHT:      In this work, we present an online planning and execution system for robots faced with these challenges.

31, TITLE:      Estimating Mass Distribution of Articulated Objects through Non-prehensile Manipulation
http://arxiv.org/abs/1907.03964
AUTHORS:        K. Niranjan Kumar ; Irfan Essa ; C. Karen Liu
HIGHLIGHT:      We explore the problem of estimating the mass distribution of an articulated object by an interactive robotic agent.

32, TITLE:      DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans
http://arxiv.org/abs/1906.06962
AUTHORS:        Ayush Dewan ; Wolfram Burgard
COMMENTS:       Accepted for ICRA-2020. Code and dataset available at https://github.com/ayushais/DBLiDARNet. Added results for Semantic Kitti Dataset
HIGHLIGHT:      In this paper, we propose a deep convolutional neural network (DCNN) for the semantic segmentation of a LiDAR scan into the classes car, pedestrian or bicyclist.

33, TITLE:      LIT: Light-field Inference of Transparency for Refractive Object Localization
http://arxiv.org/abs/1910.00721
AUTHORS:        Zheming Zhou ; Xiaotong Chen ; Odest Chadwicke Jenkins
HIGHLIGHT:      In this work, we propose LIT, a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering.

34, TITLE:      Building a COVID-19 Vulnerability Index
http://arxiv.org/abs/2003.07347
AUTHORS:        Dave DeCaprio ; Joseph Gartner ; Thadeus Burgess ; Sarthak Kothari ; Shaayan Sayed ; Carol J. McCall
HIGHLIGHT:      We present the results for three models predicting such complications, with each model having varying levels of predictive effectiveness at the expense of ease of implementation.

35, TITLE:      Fully Quantized Transformer for Machine Translation
http://arxiv.org/abs/1910.10485
AUTHORS:        Gabriele Prato ; Ella Charlaix ; Mehdi Rezagholizadeh
HIGHLIGHT:      To this end, we propose FullyQT: an all-inclusive quantization strategy for the Transformer.

36, 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 ; Erjin Zhou ; 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.

37, TITLE:      Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
http://arxiv.org/abs/1909.09287
AUTHORS:        Huan Lei ; Naveed Akhtar ; Ajmal Mian
COMMENTS:       Accepted to TPAMI
HIGHLIGHT:      We propose a spherical kernel for efficient graph convolution of 3D point clouds.

38, TITLE:      Instant recovery of shape from spectrum via latent space connections
http://arxiv.org/abs/2003.06523
AUTHORS:        Riccardo Marin ; Arianna Rampini ; Umberto Castellani ; Emanuele Rodolà ; Maks Ovsjanikov ; Simone Melzi
HIGHLIGHT:      We introduce the first learning-based method for recovering shapes from Laplacian spectra.

39, TITLE:      Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning
http://arxiv.org/abs/1902.05357
AUTHORS:        Zhiqian Chen ; Gaurav Kolhe ; Setareh Rafatirad ; Sai Manoj P. D. ; Houman Homayoun ; Liang Zhao ; Chang-Tien Lu
COMMENTS:       Design, Automation and Test in Europe (DATE) 2020
HIGHLIGHT:      To address the above mentioned challenges, this work proposes the first machine-learning framework that predicts the deobfuscation runtime based on graph deep learning techniques.

40, TITLE:      Automatic Identification of Types of Alterations in Historical Manuscripts
http://arxiv.org/abs/2003.09136
AUTHORS:        David Lassner ; Anne Baillot ; Sergej Dogadov ; Klaus-Robert Müller ; Shinichi Nakajima
HIGHLIGHT:      In this paper, we present a machine learning-based approach to help categorize alterations in documents.

41, TITLE:      A Baseline for Few-Shot Image Classification
http://arxiv.org/abs/1909.02729
AUTHORS:        Guneet S. Dhillon ; Pratik Chaudhari ; Avinash Ravichandran ; Stefano Soatto
HIGHLIGHT:      We do not advocate our approach as the solution for few-shot learning, but simply use the results to highlight limitations of current benchmarks and few-shot protocols.

42, TITLE:      Mutual Information Maximization in Graph Neural Networks
http://arxiv.org/abs/1905.08509
AUTHORS:        Xinhan Di ; Pengqian Yu ; Mingchao Sun ; Rui Bu
COMMENTS:       Accepted for presentation at IJCNN 2020
HIGHLIGHT:      We propose a new approach of enlarging the normal neighborhood in the aggregation of GNNs, which aims at maximizing mutual information.

43, TITLE:      RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices
http://arxiv.org/abs/1911.08855
AUTHORS:        Chen Chen ; Mengyuan Liu ; Xiandong Meng ; Wanpeng Xiao ; Qi Ju
COMMENTS:       16 pages, 8 figures
HIGHLIGHT:      After investigating the concern gaps between classification networks and detection backbones, and following the design principles of efficient networks, we propose a lightweight residual-like backbone with large receptive fields and wide dimensions for low-level features, which are crucial for detection tasks.

44, TITLE:      A Deep Journey into Super-resolution: A survey
http://arxiv.org/abs/1904.07523
AUTHORS:        Saeed Anwar ; Salman Khan ; Nick Barnes
COMMENTS:       Accepted in ACM Computing Surveys
HIGHLIGHT:      We introduce a taxonomy for deep-learning based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based and adversarial designs.

45, TITLE:      Masked Face Recognition Dataset and Application
http://arxiv.org/abs/2003.09093
AUTHORS:        Zhongyuan Wang ; Guangcheng Wang ; Baojin Huang ; Zhangyang Xiong ; Qi Hong ; Hao Wu ; Peng Yi ; Kui Jiang ; Nanxi Wang ; Yingjiao Pei ; Heling Chen ; Yu Miao ; Zhibing Huang ; Jinbi Liang
HIGHLIGHT:      To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD).

46, TITLE:      Resolution Adaptive Networks for Efficient Inference
http://arxiv.org/abs/2003.07326
AUTHORS:        Le Yang ; Yizeng Han ; Xi Chen ; Shiji Song ; Jifeng Dai ; Gao Huang
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we focus on spatial redundancy of input samples and propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs containing large objects with prototypical features, while only some "hard" samples need spatially detailed information.

47, TITLE:      MDLatLRR: A novel decomposition method for infrared and visible image fusion
http://arxiv.org/abs/1811.02291
AUTHORS:        Hui Li ; Xiao-Jun Wu ; Josef Kittler
COMMENTS:       IEEE Trans. Image Processing 2020, 14 pages, 17 figures, 3 tables
HIGHLIGHT:      We propose a multi-level image decomposition method based on latent low-rank representation(LatLRR), which is called MDLatLRR.

48, TITLE:      Real Image Denoising with Feature Attention
http://arxiv.org/abs/1904.07396
AUTHORS:        Saeed Anwar ; Nick Barnes
COMMENTS:       Accepted in ICCV (Oral), 2019
HIGHLIGHT:      To advance the practicability of denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture.

49, TITLE:      Ethics in the digital era
http://arxiv.org/abs/2003.06530
AUTHORS:        David Pastor-Escuredo
HIGHLIGHT:      We now face global challenges that affect groups and individuals, specially those that are most vulnerable, but cannot reduced only to individual-oriented solutions.

50, TITLE:      How incomputable is Kolmogorov complexity?
http://arxiv.org/abs/2002.07674
AUTHORS:        Paul Vitanyi
COMMENTS:       9 pages LaTeX
HIGHLIGHT:      We discuss the incomputabilty of Kolmogorov complexity, which formal loopholes this leaves us, recent approaches to compute or approximate Kolmogorov complexity, which approaches are problematic and which approaches are viable.

51, TITLE:      Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
http://arxiv.org/abs/2002.03647
AUTHORS:        Víctor Campos ; Alexander Trott ; Caiming Xiong ; Richard Socher ; Xavier Giro-i-Nieto ; Jordi Torres
COMMENTS:       17 pages, 11 figures. Code is publicly available at https://github.com/victorcampos7/edl
HIGHLIGHT:      In light of this, we propose 'Explore, Discover and Learn' (EDL), an alternative approach to information-theoretic skill discovery.

52, TITLE:      Human Posture Recognition and Gesture Imitation with a Humanoid Robot
http://arxiv.org/abs/2002.01779
AUTHORS:        Amir Aly
COMMENTS:       University of Paris 6 (UPMC), University of Sorbonne, France
HIGHLIGHT:      This study proposes different approaches for static and dynamic gesture analysis and imitation with the social robot Nao

53, TITLE:      Multimodal Intelligence: Representation Learning, Information Fusion, and Applications
http://arxiv.org/abs/1911.03977
AUTHORS:        Chao Zhang ; Zichao Yang ; Xiaodong He ; Li Deng
HIGHLIGHT:      In this paper, a technical review of the models and learning methods for multimodal intelligence is provided.

54, TITLE:      Convex Hull Monte-Carlo Tree Search
http://arxiv.org/abs/2003.04445
AUTHORS:        Michael Painter ; Bruno Lacerda ; Nick Hawes
COMMENTS:       Camera-ready version of paper accepted to ICAPS 2020, along with relevant appendices
HIGHLIGHT:      We propose the Convex Hull Monte-Carlo Tree-Search (CHMCTS) framework, which builds upon Trial Based Heuristic Tree Search and Convex Hull Value Iteration (CHVI), as a solution to multi-objective planning in large environments.

55, TITLE:      Representing Attitudes Towards Ambiguity in Hilbert Space: Foundations and Applications
http://arxiv.org/abs/1907.06314
AUTHORS:        Sandro Sozzo
COMMENTS:       23 pages, standard LaTeX, 1 figure
HIGHLIGHT:      We provide here a general mathematical framework to model attitudes towards ambiguity which uses the formalism of quantum theory as a ``purely mathematical formalism, detached from any physical interpretation''.

56, TITLE:      Pedestrian Detection: The Elephant In The Room
http://arxiv.org/abs/2003.08799
AUTHORS:        Irtiza Hasan ; Shengcai Liao ; Jinpeng Li ; Saad Ullah Akram ; Ling Shao
COMMENTS:       17 pages, 1 figure
HIGHLIGHT:      To this end, we conduct a comprehensive study in this paper, using a general principle of direct cross-dataset evaluation.

57, TITLE:      Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection
http://arxiv.org/abs/2003.08777
AUTHORS:        Zongxian Li ; Qixiang Ye ; Chong Zhang ; Jingjing Liu ; Shijian Lu ; Yonghong Tian
HIGHLIGHT:      In this work, we propose a Self-Guided Adaptation (SGA) model, target at aligning feature representation and transferring object detection models across domains while considering the instantaneous alignment difficulty.

58, TITLE:      An Empirical Evaluation of Perturbation-based Defenses
http://arxiv.org/abs/2002.03080
AUTHORS:        Adam Dziedzic ; Sanjay Krishnan
HIGHLIGHT:      Based on these insights, we demonstrate a new robust model built on noise injection and adversarial training that achieves state-of-the-art robustness.

59, TITLE:      Convolutional Tensor-Train LSTM for Spatio-temporal Learning
http://arxiv.org/abs/2002.09131
AUTHORS:        Jiahao Su ; Wonmin Byeon ; Furong Huang ; Jan Kautz ; Animashree Anandkumar
COMMENTS:       Jiahao Su and Wonmin Byeon contributed equally to this work. 17 pages, 14 figures
HIGHLIGHT:      To address this issue, we propose Convolutional Tensor-Train Decomposition (CTTD), a novel tensor decomposition with convolutional operations.

60, TITLE:      Ensemble learning in CNN augmented with fully connected subnetworks
http://arxiv.org/abs/2003.08562
AUTHORS:        Daiki Hirata ; Norikazu Takahashi
COMMENTS:       6 pages, 2 figures, 5 tables
HIGHLIGHT:      In this paper, we propose a new model called EnsNet which is composed of one base CNN and multiple Fully Connected SubNetworks (FCSNs).

61, TITLE:      Least squares binary quantization of neural networks
http://arxiv.org/abs/2001.02786
AUTHORS:        Hadi Pouransari ; Zhucheng Tu ; Oncel Tuzel
HIGHLIGHT:      In this work, we focus on the binary quantization, in which values are mapped to -1 and 1.

62, TITLE:      PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
http://arxiv.org/abs/1802.08705
AUTHORS:        Caelan Reed Garrett ; Tomás Lozano-Pérez ; Leslie Pack Kaelbling
COMMENTS:       International Conference on Automated Planning and Scheduling (ICAPS) 2020
HIGHLIGHT:      We provide domain-independent algorithms that reduce PDDLStream problems to a sequence of finite PDDL problems.

63, TITLE:      A Context-Aware Loss Function for Action Spotting in Soccer Videos
http://arxiv.org/abs/1912.01326
AUTHORS:        Anthony Cioppa ; Adrien Deliège ; Silvio Giancola ; Bernard Ghanem ; Marc Van Droogenbroeck ; Rikke Gade ; Thomas B. Moeslund
COMMENTS:       Accepted for CVPR2020 main conference. This document contains 8 pages + references + supplementary material
HIGHLIGHT:      In this paper, we propose a novel loss function that specifically considers the temporal context naturally present around each action, rather than focusing on the single annotated frame to spot.

64, TITLE:      Fairness by Learning Orthogonal Disentangled Representations
http://arxiv.org/abs/2003.05707
AUTHORS:        Mhd Hasan Sarhan ; Nassir Navab ; Abouzar Eslami ; Shadi Albarqouni
HIGHLIGHT:      In this paper, we propose a novel disentanglement approach to invariant representation problem.

65, TITLE:      Generating new concepts with hybrid neuro-symbolic models
http://arxiv.org/abs/2003.08978
AUTHORS:        Reuben Feinman ; Brenden M. Lake
HIGHLIGHT:      In this paper, we explore a synthesis of these two traditions through a novel neuro-symbolic model for generating new concepts.

66, TITLE:      ptype: Probabilistic Type Inference
http://arxiv.org/abs/1911.10081
AUTHORS:        Taha Ceritli ; Christopher K. I. Williams ; James Geddes
HIGHLIGHT:      In this paper, we propose ptype, a probabilistic robust type inference method that allows us to detect such entries, and infer data types.

67, TITLE:      dpVAEs: Fixing Sample Generation for Regularized VAEs
http://arxiv.org/abs/1911.10506
AUTHORS:        Riddhish Bhalodia ; Iain Lee ; Shireen Elhabian
HIGHLIGHT:      In this paper, we explore this representation-generation trade-off for regularized VAEs and introduce a new family of priors, namely decoupled priors, or dpVAEs, that decouple the representation space from the generation space.

68, TITLE:      Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
http://arxiv.org/abs/2002.06753
AUTHORS:        Micah Goldblum ; Steven Reich ; Liam Fowl ; Renkun Ni ; Valeriia Cherepanova ; Tom Goldstein
HIGHLIGHT:      We develop a better understanding of the underlying mechanics of meta-learning and the difference between models trained using meta-learning and models which are trained classically.

69, TITLE:      Multi-modal Synthesis of Regular Expressions
http://arxiv.org/abs/1908.03316
AUTHORS:        Qiaochu Chen ; Xinyu Wang ; Xi Ye ; Greg Durrett ; Isil Dillig
COMMENTS:       36 pages
HIGHLIGHT:      In this paper, we propose a multi-modal synthesis technique for automatically constructing regular expressions (regexes) from a combination of examples and natural language.

70, TITLE:      Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators
http://arxiv.org/abs/1911.00139
AUTHORS:        Weiwen Jiang ; Qiuwen Lou ; Zheyu Yan ; Lei Yang ; Jingtong Hu ; Xiaobo Sharon Hu ; Yiyu Shi
COMMENTS:       10 pages, 6 figures
HIGHLIGHT:      To address these challenges, we propose a cross-layer exploration framework, namely NACIM, which jointly explores device, circuit and architecture design space and takes device variation into consideration to find the most robust neural architectures.

71, TITLE:      Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks
http://arxiv.org/abs/1907.12659
AUTHORS:        Bin Wang ; Bing Xue ; Mengjie Zhang
COMMENTS:       To appear in ieee wcci 2020
HIGHLIGHT:      In this paper, an efficient particle swarm optimisation method named EPSOCNN is proposed to evolve CNN architectures inspired by the idea of transfer learning.

72, TITLE:      Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
http://arxiv.org/abs/1807.09441
AUTHORS:        Xingang Pan ; Ping Luo ; Jianping Shi ; Xiaoou Tang
COMMENTS:       Accepted for publication at ECCV 2018
HIGHLIGHT:      Unlike existing works that designed CNN architectures to improve performance on a single task of a single domain and not generalizable, we present IBN-Net, a novel convolutional architecture, which remarkably enhances a CNN's modeling ability on one domain (e.g. Cityscapes) as well as its generalization capacity on another domain (e.g. GTA5) without finetuning.

73, TITLE:      Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
http://arxiv.org/abs/1710.10121
AUTHORS:        Yiping Lu ; Aoxiao Zhong ; Quanzheng Li ; Bin Dong
HIGHLIGHT:      As an example, we propose a linear multi-step architecture (LM-architecture) which is inspired by the linear multi-step method solving ordinary differential equations.

74, TITLE:      Towards Train-Test Consistency for Semi-supervised Temporal Action Localization
http://arxiv.org/abs/1910.11285
AUTHORS:        Xudong Lin ; Zheng Shou ; Shih-Fu Chang
COMMENTS:       Work in progress
HIGHLIGHT:      In this paper, we propose a Train-Test Consistent framework, TTC-Loc.

75, TITLE:      Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning
http://arxiv.org/abs/1904.06627
AUTHORS:        Xun Wang ; Xintong Han ; Weilin Huang ; Dengke Dong ; Matthew R. Scott
COMMENTS:       Accepted CVPR 2019, rewrite main method to be more clear
HIGHLIGHT:      In this paper, we provide a general weighting framework for understanding recent pair-based loss functions.

76, TITLE:      Deeply Activated Salient Region for Instance Search
http://arxiv.org/abs/2002.00185
AUTHORS:        Hui-Chu Xiao ; Wan-Lei Zhao ; Jie Lin ; Chong-Wah Ngo
COMMENTS:       11 pages, 8 figures
HIGHLIGHT:      In this paper, a simple but effective instance-level feature representation is presented.

77, TITLE:      Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization
http://arxiv.org/abs/2002.12133
AUTHORS:        Aritz D. Martinez ; Eneko Osaba ; Javier Del Ser ; Francisco Herrera
COMMENTS:       8 pages, 5 figures, submitted to IEEE Conference on Evolutionary Computation 2020 (IEEE CEC)
HIGHLIGHT:      This work proposes a MFO framework capable of simultaneously evolving several DQL models towards solving interrelated RL tasks.

78, TITLE:      From Video Game to Real Robot: The Transfer between Action Spaces
http://arxiv.org/abs/1905.00741
AUTHORS:        Janne Karttunen ; Anssi Kanervisto ; Ville Kyrki ; Ville Hautamäki
COMMENTS:       Two first authors contributed equally. Accepted by ICASSP 2020
HIGHLIGHT:      In this work, we study how general video games can be directly used instead of fine-tuned simulations for the sim-to-real transfer.

79, TITLE:      Social navigation with human empowerment driven reinforcement learning
http://arxiv.org/abs/2003.08158
AUTHORS:        Tessa van der Heiden ; Christian Weiss ; Naveen Nagaraja Shankar ; Herke van Hoof
HIGHLIGHT:      In this work, we go beyond this approach and provide the agent with intrinsic motivation using empowerment.

80, TITLE:      On Exact Reznick, Hilbert-Artin and Putinar's Representations
http://arxiv.org/abs/1811.10062
AUTHORS:        Victor Magron ; Mohab Safey El Din
COMMENTS:       35 pages, 4 tables, extended version of the paper from ISSAC'18 conference (available at arXiv::1802.10339)
HIGHLIGHT:      We consider the problem of finding exact sums of squares (SOS) decompositions for certain classes of non-negative multivariate polynomials, relying on semidefinite programming (SDP) solvers.

81, TITLE:      An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset
http://arxiv.org/abs/2002.05878
AUTHORS:        Zhicheng Gu ; Zhihao Li ; Xuan Di ; Rongye Shi
HIGHLIGHT:      Thus, this paper introduces an approach to learn a long short-term memory (LSTM)-based model for imitating the behavior of Waymo's self-driving model.

82, TITLE:      Learning to Segment 3D Point Clouds in 2D Image Space
http://arxiv.org/abs/2003.05593
AUTHORS:        Yecheng Lyu ; Xinming Huang ; Ziming Zhang
COMMENTS:       Accepted to CVPR 2020 as oral
HIGHLIGHT:      In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively and efficiently project such point clouds into a 2D image space so that traditional 2D convolutional neural networks (CNNs) such as U-Net can be applied for segmentation.

83, TITLE:      CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
http://arxiv.org/abs/2001.02643
AUTHORS:        Florian Kluger ; Eric Brachmann ; Hanno Ackermann ; Carsten Rother ; Michael Ying Yang ; Bodo Rosenhahn
COMMENTS:       CVPR 2020
HIGHLIGHT:      We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements.

84, TITLE:      PIV-Based 3D Fluid Flow Reconstruction Using Light Field Camera
http://arxiv.org/abs/1904.06841
AUTHORS:        Zhong Li ; Jinwei Ye ; Yu Ji ; Hao Sheng ; Jingyi Yu
COMMENTS:       This submission need to be withdrawn due to an unresolved conflict between authors. This article was submitted without consent of Jinwei Ye
HIGHLIGHT:      In this paper, we present a PIV solution that uses densely sampled light field to reconstruct and track 3D particles.

85, TITLE:      BABO: Background Activation Black-Out for Efficient Object Detection
http://arxiv.org/abs/2002.01609
AUTHORS:        Byungseok Roh ; Han-Cheol Cho ; Myung-Ho Ju ; Soon Hyung Pyo
COMMENTS:       14 pages, 6 figures, 7 tables
HIGHLIGHT:      In this paper, we present an objectness-aware object detection method to reduce computational cost by sparsifying activation values on background regions where target objects don't exist.

86, TITLE:      RUBi: Reducing Unimodal Biases in Visual Question Answering
http://arxiv.org/abs/1906.10169
AUTHORS:        Remi Cadene ; Corentin Dancette ; Hedi Ben-younes ; Matthieu Cord ; Devi Parikh
COMMENTS:       NeurIPS 2019 http://papers.nips.cc/paper/8371-rubi-reducing-unimodal-biases-for-visual-question-answering
HIGHLIGHT:      We propose RUBi, a new learning strategy to reduce biases in any VQA model.

87, TITLE:      Privileged Pooling: Supervised attention-based pooling for compensating dataset bias
http://arxiv.org/abs/2003.09168
AUTHORS:        Andres C. Rodriguez ; Stefano D'Aronco ; Konrad Schindler ; Jan Dirk Wegner
COMMENTS:       privileged pooling, weakly-supervised attention, training set bias, fine-grained species recognition, camera trap imagery
HIGHLIGHT:      In this paper we propose a novel supervised image classification method that overcomes dataset bias and scarcity of training data using privileged information in the form of keypoints annotations.

88, TITLE:      Guided Zoom: Questioning Network Evidence for Fine-grained Classification
http://arxiv.org/abs/1812.02626
AUTHORS:        Sarah Adel Bargal ; Andrea Zunino ; Vitali Petsiuk ; Jianming Zhang ; Kate Saenko ; Vittorio Murino ; Stan Sclaroff
COMMENTS:       BMVC 2019 Camera Ready Version
HIGHLIGHT:      We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions.

89, TITLE:      High-Resolution Daytime Translation Without Domain Labels
http://arxiv.org/abs/2003.08791
AUTHORS:        Ivan Anokhin ; Pavel Solovev ; Denis Korzhenkov ; Alexey Kharlamov ; Taras Khakhulin ; Alexey Silvestrov ; Sergey Nikolenko ; Victor Lempitsky ; Gleb Sterkin
COMMENTS:       accepted to CVPR 2020
HIGHLIGHT:      We present the high-resolution daytime translation (HiDT) model for this task.

90, TITLE:      Efficient and Robust Shape Correspondence via Sparsity-Enforced Quadratic Assignment
http://arxiv.org/abs/2003.08680
AUTHORS:        Rui Xiang ; Rongjie Lai ; Hongkai Zhao
COMMENTS:       8 pages, 6 figures. Compared to the version to be published in the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Figure 1 has been changed to a more illustrative example and run time table 1 has been updated by our recently optimized code
HIGHLIGHT:      We use various experiments to show the efficiency, quality, and versatility of our method on large data sets, patches, and point clouds (without global meshes).

91, TITLE:      Are Registration Uncertainty and Error Monotonically Associated
http://arxiv.org/abs/1908.07709
AUTHORS:        Jie Luo ; Sarah Frisken ; Duo Wang ; Alexandra Golby ; Masashi Sugiyama ; William M. Wells III
COMMENTS:       Draft Ver.2
HIGHLIGHT:      In this pilot study, we attempt to address this fundamental problem by looking at one PIR method, the Gaussian process (GP) registration.

92, TITLE:      Leveraging Frequency Analysis for Deep Fake Image Recognition
http://arxiv.org/abs/2003.08685
AUTHORS:        Joel Frank ; Thorsten Eisenhofer ; Lea Schönherr ; Asja Fischer ; Dorothea Kolossa ; Thorsten Holz
COMMENTS:       For accompanying code see https://github.com/RUB-SysSec/GANDCTAnalysis. 13 pages, 7 figures
HIGHLIGHT:      In this paper, we address this shortcoming and our results reveal that in frequency space, GAN-generated images exhibit severe artifacts that can be easily identified.

93, TITLE:      Meshed-Memory Transformer for Image Captioning
http://arxiv.org/abs/1912.08226
AUTHORS:        Marcella Cornia ; Matteo Stefanini ; Lorenzo Baraldi ; Rita Cucchiara
COMMENTS:       CVPR 2020
HIGHLIGHT:      With the aim of filling this gap, we present M$^2$ - a Meshed Transformer with Memory for Image Captioning.

94, TITLE:      Single-Stage 6D Object Pose Estimation
http://arxiv.org/abs/1911.08324
AUTHORS:        Yinlin Hu ; Pascal Fua ; Wei Wang ; Mathieu Salzmann
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we introduce a deep architecture that directly regresses 6D poses from correspondences.

95, TITLE:      DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling
http://arxiv.org/abs/1911.12736
AUTHORS:        Xin Huang ; Stephen G. McGill ; Jonathan A. DeCastro ; Luke Fletcher ; John J. Leonard ; Brian C. Williams ; Guy Rosman
COMMENTS:       8 pages, 5 figures, 1 table
HIGHLIGHT:      In this work, we devise a novel approach for generating realistic and diverse vehicle trajectories.