2020.07.23.txt

==========New Papers==========
1, TITLE:       MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint
http://arxiv.org/abs/2007.11180
AUTHORS:        Xinpeng Xie ; Jiawei Chen ; Yuexiang Li ; Linlin Shen ; Kai Ma ; Yefeng Zheng
COMMENTS:       MICCAI 2020; The first two authors contributed equally
HIGHLIGHT:      In this paper, we propose a novel GAN (namely MI$^2$GAN) to maintain image-contents during cross-domain I2I translation.

2, TITLE:       Deep-VFX: Deep Action Recognition Driven VFX for Short Video
http://arxiv.org/abs/2007.11257
AUTHORS:        Ao Luo ; Ning Xie ; Zhijia Tao ; Feng Jiang
HIGHLIGHT:      We propose the AI method to improve this VFX synthesis.

3, TITLE:       Adversarial Training Reduces Information and Improves Transferability
http://arxiv.org/abs/2007.11259
AUTHORS:        Matteo Terzi ; Alessandro Achille ; Marco Maggipinto ; Gian Antonio Susto
COMMENTS:       Submitted to NeurIPS 2020
HIGHLIGHT:      The latter property may seem counter-intuitive as it is widely accepted by the community that classification models should only capture the minimal information (features) required for the task.

4, TITLE:       Learning Object Relation Graph and Tentative Policy for Visual Navigation
http://arxiv.org/abs/2007.11018
AUTHORS:        Heming Du ; Xin Yu ; Liang Zheng
HIGHLIGHT:      Aiming to improve these two components, this paper proposes three complementary techniques, object relation graph (ORG), trial-driven imitation learning (IL), and a memory-augmented tentative policy network (TPN).

5, TITLE:       Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction
http://arxiv.org/abs/2007.11245
AUTHORS:        Yunmei Chen ; Hongcheng Liu ; Xiaojing Ye ; Qingchao Zhang
HIGHLIGHT:      We propose a general learning based framework for solving nonsmooth and nonconvex image reconstruction problems.

6, TITLE:       Fragments-Expert: A Graphical User Interface MATLAB Toolbox for Classification of File Fragments
http://arxiv.org/abs/2007.11246
AUTHORS:        Mehdi Teimouri ; Zahra Seyedghorban ; Fatemeh Amirjani
COMMENTS:       47 Pages, 34 Figures, and 3 Tables
HIGHLIGHT:      In this paper, we present Fragments-Expert that is a graphical user interface MATLAB toolbox for the classification of file fragments.

7, TITLE:       Accelerating Deep Learning Applications in Space
http://arxiv.org/abs/2007.11089
AUTHORS:        Martina Lofqvist ; José Cano
COMMENTS:       Published as a workshop paper at SmallSat 2020 - The 34th Annual Small Satellite Conference. 19 pages, 22 figures
HIGHLIGHT:      In this paper, we investigate the performance of CNN-based object detectors on constrained devices when applying different image compression techniques.

8, TITLE:       Edge-aware Graph Representation Learning and Reasoning for Face Parsing
http://arxiv.org/abs/2007.11240
AUTHORS:        Gusi Te ; Yinglu Liu ; Wei Hu ; Hailin Shi ; Tao Mei
COMMENTS:       ECCV 2020
HIGHLIGHT:      To this end, we propose to model and reason the region-wise relations by learning graph representations, and leverage the edge information between regions for optimized abstraction.

9, TITLE:       FedOCR: Communication-Efficient Federated Learning for Scene Text Recognition
http://arxiv.org/abs/2007.11462
AUTHORS:        Wenqing Zhang ; Yang Qiu ; Song Bai ; Rui Zhang ; Xiaolin Wei ; Xiang Bai
HIGHLIGHT:      In this paper, we study how to make use of decentralized datasets for training a robust scene text recognizer while keeping them stay on local devices.

10, TITLE:      SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
http://arxiv.org/abs/2007.11464
AUTHORS:        Dominik Schlechtweg ; Barbara McGillivray ; Simon Hengchen ; Haim Dubossarsky ; Nina Tahmasebi
COMMENTS:       SemEval@COLING2020, 12 pages
HIGHLIGHT:      We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish.

11, TITLE:      Greenhouse Segmentation on High-Resolution Optical Satellite Imagery using Deep Learning Techniques
http://arxiv.org/abs/2007.11222
AUTHORS:        Orkhan Baghirli ; Imran Ibrahimli ; Tarlan Mammadzada
COMMENTS:       12 pages, 14 Figures, 3 Tables, uses arxiv.sty
HIGHLIGHT:      In this paper, a sound methodology is proposed for pixel-wise classification on images acquired by the Azersky (SPOT-7) optical satellite.

12, TITLE:      Watchlist Risk Assessment using Multiparametric Cost and Relative Entropy
http://arxiv.org/abs/2007.11328
AUTHORS:        K. Lai ; S. N. Yanushkevich
HIGHLIGHT:      The key contributions of this paper are the novel techniques for design and analysis of the biometric-enabled watchlist and the supporting infrastructure, as well as measuring the impersonation impact on e-border performance.

13, TITLE:      Wasserstein Routed Capsule Networks
http://arxiv.org/abs/2007.11465
AUTHORS:        Alexander Fuchs ; Franz Pernkopf
COMMENTS:       8 pages, 3 figures
HIGHLIGHT:      We propose a new parameter efficient capsule architecture, that is able to tackle complex tasks by using neural networks trained with an approximate Wasserstein objective to dynamically select capsules throughout the entire architecture.

14, TITLE:      Risk Assessment in the Face-based Watchlist Screening in e-Border
http://arxiv.org/abs/2007.11323
AUTHORS:        Kenneth Lai ; Svetlana N. Yanushkevich ; Vlad Shmerko
HIGHLIGHT:      To address this problem, we developed a novel cost-based model of traveler risk assessment and proved its efficiency via intensive experiments using large-scale facial databases.

15, TITLE:      One Click Lesion RECIST Measurement and Segmentation on CT Scans
http://arxiv.org/abs/2007.11087
AUTHORS:        Youbao Tang ; Ke Yan ; Jing Xiao ; Ranold M. Summers
HIGHLIGHT:      We propose a unified framework named SEENet for semi-automatic lesion \textit{SE}gmentation and RECIST \textit{E}stimation on a variety of lesions over the entire human body.

16, TITLE:      Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis
http://arxiv.org/abs/2007.11067
AUTHORS:        Xiaomeng Li ; Mengyu Jia ; Md Tauhidul Islam ; Lequan Yu ; Lei Xing
COMMENTS:       IEEE Transactions on Medical Imaging, code is at https://github.com/xmengli999/self_supervised
HIGHLIGHT:      Considering that the diagnostics of various vitreoretinal diseases can greatly benefit from another imaging modality, e.g., FFA, this paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis.

17, TITLE:      How to Increase Interest in Studying Functional Programming via Interdisciplinary Application
http://arxiv.org/abs/2007.11070
AUTHORS:        Pedro Figueirêdo ; Yuri Kim ; Le Minh Nghia ; Evan Sitt ; Xue Ying ; Viktória Zsók
COMMENTS:       18 pages, 7 figures
HIGHLIGHT:      Our goal is to increase student interest in pursuing further studies in functional programming with the use of an application: the ray tracer.

18, TITLE:      Neural Sparse Voxel Fields
http://arxiv.org/abs/2007.11571
AUTHORS:        Lingjie Liu ; Jiatao Gu ; Kyaw Zaw Lin ; Tat-Seng Chua ; Christian Theobalt
COMMENTS:       20 pages, in progress
HIGHLIGHT:      In this work, we introduce Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering.

19, TITLE:      Will Your Forthcoming Book be Successful? Predicting Book Success with CNN and Readability Scores
http://arxiv.org/abs/2007.11073
AUTHORS:        Muhammad Khalifa ; Aminul Islam
HIGHLIGHT:      We propose a model that leverages Convolutional Neural Networks along with readability indices.

20, TITLE:      Multi-Spectral Facial Biometrics in Access Control
http://arxiv.org/abs/2007.11318
AUTHORS:        K. Lai ; S. Samoil ; S. N. Yanushkevich
HIGHLIGHT:      This study demonstrates how facial biometrics, acquired using multi-spectral sensors, such as RGB, depth, and infrared, assist the data accumulation in the process of authorizing users of automated and semi-automated access systems.

21, TITLE:      Real-Time Instrument Segmentation in Robotic Surgery using Auxiliary Supervised Deep Adversarial Learning
http://arxiv.org/abs/2007.11319
AUTHORS:        Mobarakol Islam ; Daniel A. Atputharuban ; Ravikiran Ramesh ; Hongliang Ren
COMMENTS:       Published in IEEE RAL
HIGHLIGHT:      We propose a multi-resolution feature fusion module (MFF) to fuse the feature maps of different dimensions and channels from the auxiliary and main branch.

22, TITLE:      BorderDet: Border Feature for Dense Object Detection
http://arxiv.org/abs/2007.11056
AUTHORS:        Han Qiu ; Yuchen Ma ; Zeming Li ; Songtao Liu ; Jian Sun
COMMENTS:       Accepted by ECCV 2020 as Oral
HIGHLIGHT:      In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature.

23, TITLE:      Explainable Rumor Detection using Inter and Intra-feature Attention Networks
http://arxiv.org/abs/2007.11057
AUTHORS:        Mingxuan Chen ; Ning Wang ; K. P. Subbalakshmi
COMMENTS:       14 pages, 6 figures, TrueFact2020(KDD2020 workshop)
HIGHLIGHT:      We tackle the problem of automated detection of rumors in social media in this paper by designing a modular explainable architecture that uses both latent and handcrafted features and can be expanded to as many new classes of features as desired.

24, TITLE:      Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification
http://arxiv.org/abs/2007.11314
AUTHORS:        Nicole Peinelt ; Dong Nguyen ; Maria Liakata
HIGHLIGHT:      We therefore propose two ways of merging topics with word embeddings (early vs. late fusion) in a new neural architecture for question paraphrase identification.

25, TITLE:      Attend and Segment: Attention Guided Active Semantic Segmentation
http://arxiv.org/abs/2007.11548
AUTHORS:        Soroush Seifi ; Tinne Tuytelaars
HIGHLIGHT:      In this paper we propose a method to gradually segment a scene given a sequence of partial observations.

26, TITLE:      DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
http://arxiv.org/abs/2007.11301
AUTHORS:        Alexandre Carlier ; Martin Danelljan ; Alexandre Alahi ; Radu Timofte
HIGHLIGHT:      In this work, we propose a novel hierarchical generative network, called DeepSVG, for complex SVG icons generation and interpolation.

27, TITLE:      Exploratory Search with Sentence Embeddings
http://arxiv.org/abs/2007.11198
AUTHORS:        Austin Silveria
HIGHLIGHT:      We propose an exploratory search system based on hierarchical clusters and document summaries using sentence embeddings.

28, TITLE:      Predicting job-hopping likelihood using answers to open-ended interview questions
http://arxiv.org/abs/2007.11189
AUTHORS:        Madhura Jayaratne ; Buddhi Jayatilleke
HIGHLIGHT:      In this work, we show that the language one uses when responding to interview questions related to situational judgment and past behaviour is predictive of their likelihood to job hop.

29, TITLE:      Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks
http://arxiv.org/abs/2007.11469
AUTHORS:        Guillaume Heusch ; Anjith George ; David Geissbuhler ; Zohreh Mostaani ; Sebastien Marcel
HIGHLIGHT:      This paper addresses the problem of face presentation attack detection using different image modalities.

30, TITLE:      Regulating human control over autonomous systems
http://arxiv.org/abs/2007.11218
AUTHORS:        Mikolaj firlej ; Araz Taeihagh
HIGHLIGHT:      This article explores the notion of human control in the United States in the two domains of defense and transportation.

31, TITLE:      Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
http://arxiv.org/abs/2007.11457
AUTHORS:        Anjith George ; Sebastien Marcel
COMMENTS:       15 pages
HIGHLIGHT:      In this work, we propose a new framework for PAD using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network (MCCNN).

32, TITLE:      Video-ception Network: Towards Multi-Scale Efficient Asymmetric Spatial-Temporal Interactions
http://arxiv.org/abs/2007.11460
AUTHORS:        Yuan Tian ; Guangzhao Zhai ; Zhiyong Gao
HIGHLIGHT:      In this paper, we propose a novel video representing method that fuses the features spatially and temporally in an asymmetric way to model action atomics spanning multi-scale spatial-temporal scales.

33, TITLE:      A Parallel Evolutionary Multiple-Try Metropolis Markov Chain Monte Carlo Algorithm for Sampling Spatial Partitions
http://arxiv.org/abs/2007.11461
AUTHORS:        Wendy K. Tam Cho ; Yan Y. Liu
HIGHLIGHT:      We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling spatial partitions that lie within a large and complex spatial state space.

34, TITLE:      Exploiting Temporal Coherence for Self-Supervised One-shot Video Re-identification
http://arxiv.org/abs/2007.11064
AUTHORS:        Dripta S. Raychaudhuri ; Amit K. Roy-Chowdhury
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      In this paper, we propose a new framework named Temporal Consistency Progressive Learning, which uses temporal coherence as a novel self-supervised auxiliary task in the one-shot learning paradigm to capture such relationships amongst the unlabeled tracklets.

35, TITLE:      IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings
http://arxiv.org/abs/2007.11201
AUTHORS:        Vishal Keswani ; Sakshi Singh ; Ashutosh Modi
COMMENTS:       6 pages, 1 figure, 4 tables. Accepted at the Second Workshop on Financial Technology and Natural Language Processing (FinNLP-2020)
HIGHLIGHT:      In this paper, we present our approaches for the FinSim 2020 shared task on "Learning Semantic Representations for the Financial Domain".

36, TITLE:      Simplifying Multiple-Statement Reductions with the Polyhedral Model
http://arxiv.org/abs/2007.11203
AUTHORS:        Cambridge Yang ; Eric Atkinson ; Michael Carbin
HIGHLIGHT:      In this work, we identify and formalize the multiple\-/statement reduction problem as a bilinear optimization problem.

37, TITLE:      An Image Analogies Approach for Multi-Scale Contour Detection
http://arxiv.org/abs/2007.11047
AUTHORS:        Slimane Larabi ; Neil M. Robertson
COMMENTS:       Not published paper
HIGHLIGHT:      In this paper we deal with contour detection based on the recent image analogy principle which has been successfully used for super-resolution, texture and curves synthesis and interactive editing.

38, TITLE:      A Framework based on Deep Neural Networks to Extract Anatomy of Mosquitoes from Images
http://arxiv.org/abs/2007.11052
AUTHORS:        Mona Minakshi ; Pratool Bharti ; Tanvir Bhuiyan ; Sherzod Kariev ; Sriram Chellappan
HIGHLIGHT:      Our techniques in this paper have practical applications in public health, taxonomy and citizen-science efforts.

39, TITLE:      Curriculum Vitae Recommendation Based on Text Mining
http://arxiv.org/abs/2007.11053
AUTHORS:        Honorio Apaza Alanoca ; Americo A. Rubin de Celis Vidal ; Josimar Edinson Chire Saire
HIGHLIGHT:      So, we use the techniques from Text Mining and Natural Language Processing.

40, TITLE:      Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration
http://arxiv.org/abs/2007.11430
AUTHORS:        Xin Li ; Xin Jin ; Jianxin Lin ; Tao Yu ; Sen Liu ; Yaojun Wu ; Wei Zhou ; Zhibo Chen
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      To decompose such interference, we introduce the concept of Disentangled Feature Learning to achieve the feature-level divide-and-conquer of hybrid distortions.

41, TITLE:      Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction
http://arxiv.org/abs/2007.11432
AUTHORS:        Bharat Lal Bhatnagar ; Cristian Sminchisescu ; Christian Theobalt ; Gerard Pons-Moll
COMMENTS:       Accepted at ECCV'20 (Oral)
HIGHLIGHT:      In this work, we present methodology that combines detail-rich implicit functions and parametric representations in order to reconstruct 3D models of people that remain controllable and accurate even in the presence of clothing.

42, TITLE:      Sistema experto para el diagnóstico de enfermedades y plagas en los cultivos del arroz, tabaco, tomate, pimiento, maíz, pepino y frijol
http://arxiv.org/abs/2007.11038
AUTHORS:        Ing. Yosvany Medina Carbó ; MSc. Iracely Milagros Santana Ges ; Lic. Saily Leo González
COMMENTS:       in Spanish
HIGHLIGHT:      This paper presents an Expert System for the diagnosis of diseases and pests in rice, tobacco, tomato, pepper, corn, cucumber and bean crops.

43, TITLE:      Directional Temporal Modeling for Action Recognition
http://arxiv.org/abs/2007.11040
AUTHORS:        Xinyu Li ; Bing Shuai ; Joseph Tighe
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we introduce a channel independent directional convolution (CIDC) operation, which learns to model the temporal evolution among local features.

44, TITLE:      Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation
http://arxiv.org/abs/2007.11514
AUTHORS:        Manish Sahu ; Ronja Strömsdörfer ; Anirban Mukhopadhyay ; Stefan Zachow
COMMENTS:       Accepted at MICCAI2020
HIGHLIGHT:      This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue.

45, TITLE:      Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition
http://arxiv.org/abs/2007.11365
AUTHORS:        Sudhakar Kumawat ; Manisha Verma ; Yuta Nakashima ; Shanmuganathan Raman
COMMENTS:       Extended version of our CVPR 2019 work
HIGHLIGHT:      To address these issues, we propose spatio-temporal short term Fourier transform (STFT) blocks, a new class of convolutional blocks that can serve as an alternative to the 3D convolutional layer and its variants in 3D CNNs.

46, TITLE:      DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration
http://arxiv.org/abs/2007.11255
AUTHORS:        Markus Horn ; Nico Engel ; Vasileios Belagiannis ; Michael Buchholz ; Klaus Dietmayer
COMMENTS:       7 pages, 5 figures, 4 tables, accepted by ITSC 2020
HIGHLIGHT:      We propose an approach to predict the alignment between two point clouds with overlapping data content, but displaced origins.

47, TITLE:      CrossTransformers: spatially-aware few-shot transfer
http://arxiv.org/abs/2007.11498
AUTHORS:        Carl Doersch ; Ankush Gupta ; Andrew Zisserman
HIGHLIGHT:      In this work, we illustrate how the neural network representations which underpin modern vision systems are subject to supervision collapse, whereby they lose any information that is not necessary for performing the training task, including information that may be necessary for transfer to new tasks or domains.

48, TITLE:      Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets
http://arxiv.org/abs/2007.11256
AUTHORS:        Tian Chen ; Shijie An ; Yuan Zhang ; Chongyang Ma ; Huayan Wang ; Xiaoyan Guo ; Wen Zheng
COMMENTS:       14 pages, 8 figures
HIGHLIGHT:      In this paper, we tackle this problem in three aspects.

49, TITLE:      Instance-aware Self-supervised Learning for Nuclei Segmentation
http://arxiv.org/abs/2007.11186
AUTHORS:        Xinpeng Xie ; Jiawei Chen ; Yuexiang Li ; Linlin Shen ; Kai Ma ; Yefeng Zheng
COMMENTS:       MICCAI 2020; The first two authors contributed equally
HIGHLIGHT:      In this paper, we propose a novel self-supervised learning framework to deeply exploit the capacity of widely-used convolutional neural networks (CNNs) on the nuclei instance segmentation task.

50, TITLE:      Creating a Large-scale Synthetic Dataset for Human Activity Recognition
http://arxiv.org/abs/2007.11118
AUTHORS:        Ollie Matthews ; Koki Ryu ; Tarun Srivastava
HIGHLIGHT:      In this paper, we approach this by using 3D rendering tools to generate a synthetic dataset of videos, and show that a classifier trained on these videos can generalise to real videos.

51, TITLE:      Interpolating GANs to Scaffold Autotelic Creativity
http://arxiv.org/abs/2007.11119
AUTHORS:        Ziv Epstein ; Océane Boulais ; Skylar Gordon ; Matt Groh
HIGHLIGHT:      We present "Meet the Ganimals," a casual creator built on interpolations of BigGAN that can generate novel, hybrid animals called ganimals by efficiently searching this possibility space.

52, TITLE:      When Classical Chinese Meets Machine Learning: Explaining the Relative Performances of Word and Sentence Segmentation Tasks
http://arxiv.org/abs/2007.11171
AUTHORS:        Chao-Lin Liu ; Chang-Ting Chu ; Wei-Ting Chang ; Ti-Yong Zheng
COMMENTS:       4 pages, 1 figure, 2 tables, 2020 International Conference on Digital Humanities (Alliance of Digital Humanities Organizations, ADHO)
HIGHLIGHT:      We consider three major text sources about the Tang Dynasty of China in our experiments that aim to segment text written in classical Chinese.

53, TITLE:      Exploiting No-Regret Algorithms in System Design
http://arxiv.org/abs/2007.11172
AUTHORS:        Le Cong Dinh ; Nick Bishop ; Long Tran-Thanh
HIGHLIGHT:      To design such a payoff matrix, we propose a novel solution that provably has a unique minimax solution with the desired behaviour.

54, TITLE:      Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop
http://arxiv.org/abs/2007.11110
AUTHORS:        Benjamin Biggs ; Oliver Boyne ; James Charles ; Andrew Fitzgibbon ; Roberto Cipolla
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      We introduce an automatic, end-to-end method for recovering the 3D pose and shape of dogs from monocular internet images.

55, TITLE:      Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning
http://arxiv.org/abs/2007.11355
AUTHORS:        Junde Wu ; Shuang Yu ; Wenting Chen ; Kai Ma ; Rao Fu ; Hanruo Liu ; Xiaoguang Di ; Yefeng Zheng
HIGHLIGHT:      In order to alleviate this problem, we propose a glaucoma classification framework which takes advantage of not only the properly labeled images, but also undiagnosed images without glaucoma labels.

56, TITLE:      Rethinking CNN Models for Audio Classification
http://arxiv.org/abs/2007.11154
AUTHORS:        Kamalesh Palanisamy ; Dipika Singhania ; Angela Yao
COMMENTS:       8 pages, 3 figures, Submitted to ICPR 2020
HIGHLIGHT:      In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used as strong baseline networks for audio classification.

57, TITLE:      Human-Centered Unsupervised Segmentation Fusion
http://arxiv.org/abs/2007.11361
AUTHORS:        Gregor Koporec ; Janez Perš
COMMENTS:       Accepted to the IROS2019 Workshop: Benchmark and Dataset for Probabilistic Prediction of Interactive Human Behavior, 5 pages
HIGHLIGHT:      In this paper, we introduce a new segmentation fusion model that is based on K-Modes clustering.

58, TITLE:      Learning Directional Feature Maps for Cardiac MRI Segmentation
http://arxiv.org/abs/2007.11349
AUTHORS:        Feng Cheng ; Cheng Chen ; Yukang Wang ; Heshui Shi ; Yukun Cao ; Dandan Tu ; Changzheng Zhang ; Yongchao Xu
COMMENTS:       Accepted by MICCAI2020
HIGHLIGHT:      To tackle these two problems, we propose a novel method to exploit the directional feature maps, which can simultaneously strengthen the differences between classes and the similarities within classes.

59, TITLE:      Unsupervised Shape and Pose Disentanglement for 3D Meshes
http://arxiv.org/abs/2007.11341
AUTHORS:        Keyang Zhou ; Bharat Lal Bhatnagar ; Gerard Pons-Moll
HIGHLIGHT:      In this paper, we present a simple yet effective approach to learn disentangled shape and pose representations in an unsupervised setting.

60, TITLE:      DEAL: Deep Evidential Active Learning for Image Classification
http://arxiv.org/abs/2007.11344
AUTHORS:        Patrick Hemmer ; Niklas Kühl ; Jakob Schöffer
HIGHLIGHT:      In this paper, we propose a novel AL algorithm that efficiently learns from unlabeled data by capturing high prediction uncertainty.

61, TITLE:      Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning
http://arxiv.org/abs/2007.11149
AUTHORS:        Sk Miraj Ahmed ; Aske R Lejbølle ; Rameswar Panda ; Amit K. Roy-Chowdhury
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      Rather, based on the fact that it is easy to store the learned re-identifications models, which mitigates any data privacy concern, we develop an efficient model adaptation approach using hypothesis transfer learning that aims to transfer the knowledge using only source models and limited labeled data, but without using any source camera data from the existing network.

62, TITLE:      Massive Multi-Document Summarization of Product Reviews with Weak Supervision
http://arxiv.org/abs/2007.11348
AUTHORS:        Ori Shapira ; Ran Levy
HIGHLIGHT:      We propose a schema for summarizing a massive set of reviews on top of a standard summarization algorithm.

63, TITLE:      Feature based Sequential Classifier with Attention Mechanism
http://arxiv.org/abs/2007.11392
AUTHORS:        Sudhir Sornapudi ; R. Joe Stanley ; William V. Stoecker ; Rodney Long ; Zhiyun Xue ; Rosemary Zuna ; Shelliane R. Frazier ; Sameer Antani
HIGHLIGHT:      To model this relationship of disease severity to spatial distribution of abnormalities, we propose a network pipeline, DeepCIN, to analyze high-resolution epithelium images (manually extracted from whole-slide images) hierarchically by focusing on localized vertical regions and fusing this local information for determining Normal/CIN classification.

64, TITLE:      When to (or not to) trust intelligent machines: Insights from an evolutionary game theory analysis of trust in repeated games
http://arxiv.org/abs/2007.11338
AUTHORS:        The Anh Han ; Cedric Perret ; Simon T. Powers
HIGHLIGHT:      Here we formalise this by using the methods of evolutionary game theory to study the viability of trust-based strategies in repeated games.

65, TITLE:      Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
http://arxiv.org/abs/2007.11330
AUTHORS:        Qing Yu ; Daiki Ikami ; Go Irie ; Kiyoharu Aizawa
COMMENTS:       ECCV 2020
HIGHLIGHT:      Instead of training an OOD detector and SSL separately, we propose a multi-task curriculum learning framework.

66, TITLE:      To Be or Not To Be a Verbal Multiword Expression: A Quest for Discriminating Features
http://arxiv.org/abs/2007.11381
AUTHORS:        Caroline Pasquer ; Agata Savary ; Jean-Yves Antoine ; Carlos Ramisch ; Nicolas Labroche ; Arnaud Giacometti
HIGHLIGHT:      Surprisingly, a simple custom frequency-based feature selection method proves more efficient than other standard methods such as Chi-squared test, information gain or decision trees.

67, TITLE:      Deep Variational Instance Segmentation
http://arxiv.org/abs/2007.11576
AUTHORS:        Jialin Yuan ; Chao Chen ; Li Fuxin
HIGHLIGHT:      In this paper, we propose a novel algorithm that directly utilizes a fully convolutional network (FCN) to predict instance labels.

68, TITLE:      FLOT: Scene Flow on Point Clouds Guided by Optimal Transport
http://arxiv.org/abs/2007.11142
AUTHORS:        Gilles Puy ; Alexandre Boulch ; Renaud Marlet
COMMENTS:       Accepted at ECCV20
HIGHLIGHT:      We propose and study a method called FLOT that estimates scene flow on point clouds.

69, TITLE:      PackIt: A Virtual Environment for Geometric Planning
http://arxiv.org/abs/2007.11121
AUTHORS:        Ankit Goyal ; Jia Deng
COMMENTS:       Accepted to ICML 2020
HIGHLIGHT:      We present PackIt, a virtual environment to evaluate and potentially learn the ability to do geometric planning, where an agent needs to take a sequence of actions to pack a set of objects into a box with limited space. We also construct a set of challenging packing tasks using an evolutionary algorithm.



==========Updates to Previous Papers==========
1, TITLE:       Learning to Exploit Multiple Vision Modalities by Using Grafted Networks
http://arxiv.org/abs/2003.10959
AUTHORS:        Yuhuang Hu ; Tobi Delbruck ; Shih-Chii Liu
COMMENTS:       Accepted at ECCV 2020, 14 pages
HIGHLIGHT:      This paper proposes a Network Grafting Algorithm (NGA), where a new front end network driven by unconventional visual inputs replaces the front end network of a pretrained deep network that processes intensity frames.

2, TITLE:       Adapting Object Detectors with Conditional Domain Normalization
http://arxiv.org/abs/2003.07071
AUTHORS:        Peng Su ; Kun Wang ; Xingyu Zeng ; Shixiang Tang ; Dapeng Chen ; Di Qiu ; Xiaogang Wang
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      In this work, we present the Conditional Domain Normalization (CDN) to bridge the domain gap.

3, TITLE:       DLow: Diversifying Latent Flows for Diverse Human Motion Prediction
http://arxiv.org/abs/2003.08386
AUTHORS:        Ye Yuan ; Kris Kitani
COMMENTS:       ECCV 2020. Project Page: https://www.ye-yuan.com/dlow
HIGHLIGHT:      To address these problems, we propose a novel sampling method, Diversifying Latent Flows (DLow), to produce a diverse set of samples from a pretrained deep generative model.

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

5, TITLE:       Provably Good Batch Reinforcement Learning Without Great Exploration
http://arxiv.org/abs/2007.08202
AUTHORS:        Yao Liu ; Adith Swaminathan ; Alekh Agarwal ; Emma Brunskill
COMMENTS:       36 pages, 7 figures
HIGHLIGHT:      Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes tasks.

6, TITLE:       Object Reachability via Swaps under Strict and Weak Preferences
http://arxiv.org/abs/1909.07557
AUTHORS:        Sen Huang ; Mingyu Xiao
COMMENTS:       This version is to appear in Autonomous Agents and Multi-Agent Systems
HIGHLIGHT:      We answer this open problem positively by giving a polynomial-time algorithm.

7, TITLE:       Partially Observable Concurrent Kleene Algebra
http://arxiv.org/abs/2007.07593
AUTHORS:        Jana Wagemaker ; Paul Brunet ; Simon Docherty ; Tobias Kappé ; Jurriaan Rot ; Alexandra Silva
COMMENTS:       Accepted for publication at CONCUR 2020
HIGHLIGHT:      We introduce partially observable concurrent Kleene algebra (POCKA), an algebraic framework to reason about concurrent programs with control structures, such as conditionals and loops.

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

9, TITLE:       Symbolic Partial-Order Execution for Testing Multi-Threaded Programs
http://arxiv.org/abs/2005.06688
AUTHORS:        Daniel Schemmel ; Julian Büning ; César Rodríguez ; David Laprell ; Klaus Wehrle
COMMENTS:       Extended version of a paper presented at CAV'20
HIGHLIGHT:      We describe a technique for systematic testing of multi-threaded programs.

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

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

12, TITLE:      Evaluating structure learning algorithms with a balanced scoring function
http://arxiv.org/abs/1905.12666
AUTHORS:        Anthony Constantinou
HIGHLIGHT:      This paper proposes the Balanced Scoring Function (BSF) that eliminates this bias by adjusting the reward function based on the difficulty of discovering an edge, or no edge, proportional to their occurrence rate in the ground truth graph.

13, TITLE:      Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer
http://arxiv.org/abs/2007.05146
AUTHORS:        Xinghao Chen ; Yiman Zhang ; Yunhe Wang ; Han Shu ; Chunjing Xu ; Chang Xu
HIGHLIGHT:      This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm.

14, TITLE:      Source Camera Verification from Strongly Stabilized Videos
http://arxiv.org/abs/1912.05018
AUTHORS:        Enes Altinisik ; Husrev Taha Sencar
HIGHLIGHT:      To address this challenge, we introduce a source camera verification method for videos that takes into account the spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search.

15, TITLE:      Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening
http://arxiv.org/abs/2007.05593
AUTHORS:        Hong Xu ; David E. Timm ; Shireen Y. Elhabian
COMMENTS:       Accepted for publication in MICCAI 2020, the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention
HIGHLIGHT:      Here, we focus on automating the early decision making for the microscope operator, scoring low magnification images of squares, and proposing the first deep learning framework, XCryoNet, for automated cryo-EM grid screening.

16, TITLE:      A SentiWordNet Strategy for Curriculum Learning in Sentiment Analysis
http://arxiv.org/abs/2005.04749
AUTHORS:        Vijjini Anvesh Rao ; Kaveri Anuranjana ; Radhika Mamidi
COMMENTS:       Accepted Short Paper at 25th International Conference on Applications of Natural Language to Information Systems, June 2020, DFKI Saarbr\"ucken, Germany
HIGHLIGHT:      In this paper, we apply the ideas of curriculum learning, driven by SentiWordNet in a sentiment analysis setting.

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

18, TITLE:      An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on Bert
http://arxiv.org/abs/2005.01006
AUTHORS:        Wei Bao ; Hongshu Che ; Jiandong Zhang
COMMENTS:       ACL-SemEval 2020
HIGHLIGHT:      We apply several methods in calculating the distance between two embedding vector generated by Bidirectional Encoder Representation from Transformer (BERT).

19, TITLE:      PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
http://arxiv.org/abs/2001.08950
AUTHORS:        Saurabh Goyal ; Anamitra R. Choudhury ; Saurabh M. Raje ; Venkatesan T. Chakaravarthy ; Yogish Sabharwal ; Ashish Verma
COMMENTS:       11 pages, 8 figures, 4 tables
HIGHLIGHT:      We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy.

20, TITLE:      Unifying Deep Local and Global Features for Image Search
http://arxiv.org/abs/2001.05027
AUTHORS:        Bingyi Cao ; Andre Araujo ; Jack Sim
COMMENTS:       ECCV'20 camera-ready
HIGHLIGHT:      In this work, our key contribution is to unify global and local features into a single deep model, enabling accurate retrieval with efficient feature extraction.

21, TITLE:      Variable Rate Deep Image Compression with Modulated Autoencoder
http://arxiv.org/abs/1912.05526
AUTHORS:        Fei Yang ; Luis Herranz ; Joost van de Weijer ; José A. Iglesias Guitián ; Antonio López ; Mikhail Mozerov
COMMENTS:       Published as a journal paper in IEEE Signal Processing Letters
HIGHLIGHT:      Addressing these limitations, we formulate the problem of variable rate-distortion optimization for deep image compression, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific rate-distortion tradeoff via a modulation network.

22, TITLE:      Finding Your (3D) Center: 3D Object Detection Using a Learned Loss
http://arxiv.org/abs/2004.02693
AUTHORS:        David Griffiths ; Jan Boehm ; Tobias Ritschel
COMMENTS:       19 pages, 8 figures, Accepted ECCV 2020
HIGHLIGHT:      Addressing this disparity, we introduce a new optimization procedure, which allows training for 3D detection with raw 3D scans while using as little as 5% of the object labels and still achieve comparable performance.

23, TITLE:      A Graph Attention Spatio-temporal Convolutional Networks for 3D Human Pose Estimation in Video
http://arxiv.org/abs/2003.14179
AUTHORS:        Junfa Liu ; Zhijun Liang ; Yihui Li ; Yisheng Guan ; Juan Rojas
COMMENTS:       19 pages, single column, 7 figures, 5 tables
HIGHLIGHT:      In this work, we improve the learning of kinematic constraints in the human skeleton; namely posture, 2nd order joint relations, and symmetry.

24, TITLE:      SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
http://arxiv.org/abs/2007.04938
AUTHORS:        Kimin Lee ; Michael Laskin ; Aravind Srinivas ; Pieter Abbeel
HIGHLIGHT:      To mitigate these issues, we present SUNRISE, a simple unified ensemble method, which is compatible with various off-policy RL algorithms.

25, TITLE:      Spatial Attention Pyramid Network for Unsupervised Domain Adaptation
http://arxiv.org/abs/2003.12979
AUTHORS:        Congcong Li ; Dawei Du ; Libo Zhang ; Longyin Wen ; Tiejian Luo ; Yanjun Wu ; Pengfei Zhu
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      To that end, in this paper, we design a new spatial attention pyramid network for unsupervised domain adaptation.

26, TITLE:      Dataset for Automatic Summarization of Russian News
http://arxiv.org/abs/2006.11063
AUTHORS:        Ilya Gusev
COMMENTS:       Version 3, accepted to AINL 2020
HIGHLIGHT:      We describe the properties of this dataset and benchmark several extractive and abstractive models.

27, TITLE:      Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques
http://arxiv.org/abs/2005.12188
AUTHORS:        Mona Minakshi ; Pratool Bharti ; Willie B. McClinton III ; Jamshidbek Mirzakhalov ; Ryan M. Carney ; Sriram Chellappan
HIGHLIGHT:      This paper presents an innovative solution to this problem.

28, TITLE:      JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset
http://arxiv.org/abs/2002.08397
AUTHORS:        Abhijeet Shenoi ; Mihir Patel ; JunYoung Gwak ; Patrick Goebel ; Amir Sadeghian ; Hamid Rezatofighi ; Roberto Martín-Martín ; Silvio Savarese
COMMENTS:       8 pages, 5 figures, 2 tables; Accepted at IROS 2020
HIGHLIGHT:      In this work we present JRMOT, a novel 3D MOT system that integrates information from RGB images and 3D point clouds to achieve real-time, state-of-the-art tracking performance. As part of our work, we release the JRDB dataset, a novel large scale 2D+3D dataset and benchmark, annotated with over 2 million boxes and 3500 time consistent 2D+3D trajectories across 54 indoor and outdoor scenes.

29, TITLE:      Neural Hair Rendering
http://arxiv.org/abs/2004.13297
AUTHORS:        Menglei Chai ; Jian Ren ; Sergey Tulyakov
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we propose a generic neural-based hair rendering pipeline that can synthesize photo-realistic images from virtual 3D hair models.

30, TITLE:      Ordered Functional Decision Diagrams: A Functional Semantics For Binary Decision Diagrams
http://arxiv.org/abs/2003.09340
AUTHORS:        Joan Thibault ; Khalil Ghorbal
HIGHLIGHT:      We introduce a novel framework, termed $\lambda$DD, that revisits Binary Decision Diagrams from a purely functional point of view.

31, TITLE:      IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic
http://arxiv.org/abs/2007.03953
AUTHORS:        Hao Wang ; Diederick Vermetten ; Furong Ye ; Carola Doerr ; Thomas Bäck
HIGHLIGHT:      We propose IOHanalyzer, a new software for analyzing the empirical performance of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, and similar optimizers.

32, TITLE:      Deep Learning Based Brain Tumor Segmentation: A Survey
http://arxiv.org/abs/2007.09479
AUTHORS:        Zhihua Liu ; Long Chen ; Lei Tong ; Feixiang Zhou ; Zheheng Jiang ; Qianni Zhang ; Caifeng Shan ; Yinhai Wang ; Xiangrong Zhang ; Ling Li ; Huiyu Zhou
HIGHLIGHT:      Considering state-of-the-art technologies and their performance, the purpose of this paper is to provide a comprehensive survey of recently developed deep learning based brain tumor segmentation techniques.

33, TITLE:      Operation-Aware Soft Channel Pruning using Differentiable Masks
http://arxiv.org/abs/2007.03938
AUTHORS:        Minsoo Kang ; Bohyung Han
COMMENTS:       ICML 2020
HIGHLIGHT:      We propose a simple but effective data-driven channel pruning algorithm, which compresses deep neural networks in a differentiable way by exploiting the characteristics of operations.

34, TITLE:      Paraphrasing Complex Network: Network Compression via Factor Transfer
http://arxiv.org/abs/1802.04977
AUTHORS:        Jangho Kim ; SeongUk Park ; Nojun Kwak
COMMENTS:       Advances in Neural Information Processing Systems
HIGHLIGHT:      In this paper, we propose a novel knowledge transfer method which uses convolutional operations to paraphrase teacher's knowledge and to translate it for the student.

35, TITLE:      Cooking Is All About People: Comment Classification On Cookery Channels Using BERT and Classification Models (Malayalam-English Mix-Code)
http://arxiv.org/abs/2007.04249
AUTHORS:        Subramaniam Kazhuparambil ; Abhishek Kaushik
COMMENTS:       Rectified typos
HIGHLIGHT:      In this work, we have evaluated top-performing classification models for classifying comments which are a mix of different combinations of English and Malayalam (only English, only Malayalam and Mix of English and Malayalam).

36, TITLE:      RAFT: Recurrent All-Pairs Field Transforms for Optical Flow
http://arxiv.org/abs/2003.12039
AUTHORS:        Zachary Teed ; Jia Deng
COMMENTS:       Accepted to ECCV 2020. Further improvement of results with better upsampling
HIGHLIGHT:      We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for optical flow.

37, TITLE:      Coinduction Plain and Simple
http://arxiv.org/abs/2007.09909
AUTHORS:        François Bry
HIGHLIGHT:      Coinduction Plain and Simple

38, TITLE:      Deep Learning for Vision-based Prediction: A Survey
http://arxiv.org/abs/2007.00095
AUTHORS:        Amir Rasouli
HIGHLIGHT:      The objective of this paper is to provide an overview of the field in the past five years with a particular focus on deep learning approaches.

39, TITLE:      Bounding the expected run-time of nonconvex optimization with early stopping
http://arxiv.org/abs/2002.08856
AUTHORS:        Thomas Flynn ; Kwang Min Yu ; Abid Malik ; Nicolas D'Imperio ; Shinjae Yoo
COMMENTS:       Camera ready version for UAI 2020
HIGHLIGHT:      We develop the approach in the general setting of a first-order optimization algorithm, with possibly biased update directions subject to a geometric drift condition.

40, TITLE:      Multi-agent model for risk prediction in surgery
http://arxiv.org/abs/2005.10738
AUTHORS:        Bruno Perez ; Julien Henriet ; Christophe Lang ; Laurent Philippe
HIGHLIGHT:      This article presents our model, its implementation and the first results obtained.

41, TITLE:      3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
http://arxiv.org/abs/1907.03961
AUTHORS:        Xinshuo Weng ; Jianren Wang ; David Held ; Kris Kitani
COMMENTS:       Accepted at IROS 2020
HIGHLIGHT:      In contrast, this work proposes a simple real-time 3D MOT system.

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

43, TITLE:      Answering Questions about Data Visualizations using Efficient Bimodal Fusion
http://arxiv.org/abs/1908.01801
AUTHORS:        Kushal Kafle ; Robik Shrestha ; Brian Price ; Scott Cohen ; Christopher Kanan
COMMENTS:       Presented at WACV, 2020
HIGHLIGHT:      Here, we propose a novel CQA algorithm called parallel recurrent fusion of image and language (PReFIL).

44, TITLE:      How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning
http://arxiv.org/abs/2005.11838
AUTHORS:        Aviad Elyashar ; Rami Puzis ; Michael Fire
COMMENTS:       arXiv admin note: text overlap with arXiv:1912.04003
HIGHLIGHT:      In this paper, we propose SpokenName2Vec, a novel and generic approach which addresses the similar name suggestion problem by utilizing automated speech generation, and deep learning to produce spoken name embeddings.

45, TITLE:      Text-Based Ideal Points
http://arxiv.org/abs/2005.04232
AUTHORS:        Keyon Vafa ; Suresh Naidu ; David M. Blei
COMMENTS:       Appeared in Proceedings of the 2020 Conference of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      In this paper, we introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors.

46, TITLE:      TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
http://arxiv.org/abs/2006.11825
AUTHORS:        Yecheng Lyu ; Ming Li ; Xinming Huang ; Ulkuhan Guler ; Patrick Schaumont ; Ziming Zhang
COMMENTS:       7 pages
HIGHLIGHT:      In contrast, in this paper we study the methods to transfer the graphs into trees so that explicit orders are learned to direct the feature integration from local to global.

47, TITLE:      Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors
http://arxiv.org/abs/1910.14667
AUTHORS:        Zuxuan Wu ; Ser-Nam Lim ; Larry Davis ; Tom Goldstein
COMMENTS:       ECCV 2020
HIGHLIGHT:      We present a systematic study of adversarial attacks on state-of-the-art object detection frameworks.

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

49, TITLE:      Maximum Cut Parameterized by Crossing Number
http://arxiv.org/abs/1903.06061
AUTHORS:        Markus Chimani ; Christine Dahn ; Martina Juhnke-Kubitzke ; Nils M. Kriege ; Petra Mutzel ; Alexander Nover
HIGHLIGHT:      We propose a fixed-parameter tractable algorithm parameterized by the number $k$ of crossings in a given drawing of $G$.

50, TITLE:      Multi-View Optimization of Local Feature Geometry
http://arxiv.org/abs/2003.08348
AUTHORS:        Mihai Dusmanu ; Johannes L. Schönberger ; Marc Pollefeys
COMMENTS:       Accepted at ECCV 2020. 28 pages, 11 figures, 6 tables
HIGHLIGHT:      In this work, we address the problem of refining the geometry of local image features from multiple views without known scene or camera geometry.

51, TITLE:      Learning towards Minimum Hyperspherical Energy
http://arxiv.org/abs/1805.09298
AUTHORS:        Weiyang Liu ; Rongmei Lin ; Zhen Liu ; Lixin Liu ; Zhiding Yu ; Bo Dai ; Le Song
COMMENTS:       NeurIPS 2018
HIGHLIGHT:      In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.

52, TITLE:      Multi-Objective level generator generation with Marahel
http://arxiv.org/abs/2005.08368
AUTHORS:        Ahmed Khalifa ; Julian Togelius
COMMENTS:       Published at the PCGWorkshop 2020, 8pages, 7 figures
HIGHLIGHT:      This paper introduces a new system to design constructive level generators by searching the space of constructive level generators defined by Marahel language.

53, TITLE:      Simultaneous robust subspace recovery and semi-stability of quiver representations
http://arxiv.org/abs/2003.02962
AUTHORS:        Calin Chindris ; Daniel Kline
HIGHLIGHT:      In this paper, we show that SRSR and the more general quiver semi-stability problem can be solved effectively.

54, TITLE:      DerainCycleGAN: A Simple Unsupervised Network for Single Image Deraining and Rainmaking
http://arxiv.org/abs/1912.07015
AUTHORS:        Yanyan Wei ; Zhao Zhang ; Yang Wang ; Jicong Fan ; Shuicheng Yan ; Meng Wang
HIGHLIGHT:      In this paper, we explore the unsupervised SID task using unpaired data and propose a novel net called Attention-guided Deraining by Constrained CycleGAN (or shortly, DerainCycleGAN), which can fully utilize the constrained transfer learning abilitiy and circulatory structure of CycleGAN.

55, TITLE:      Compressed DenseNet for Lightweight Character Recognition
http://arxiv.org/abs/1912.07016
AUTHORS:        Zhao Zhang ; Zemin Tang ; Yang Wang ; Haijun Zhang ; Shuicheng Yan ; Meng Wang
HIGHLIGHT:      In this paper, we propose a compressed convolution block called Lightweight Dense Block (LDB).

56, TITLE:      Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
http://arxiv.org/abs/1912.13503
AUTHORS:        Jeffrey O Zhang ; Alexander Sax ; Amir Zamir ; Leonidas Guibas ; Jitendra Malik
COMMENTS:       In ECCV 2020 (Spotlight). For more, see project website and code at http://sidetuning.berkeley.edu
HIGHLIGHT:      In this paper, we propose a straightforward alternative: side-tuning.

57, TITLE:      ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language
http://arxiv.org/abs/1912.08830
AUTHORS:        Dave Zhenyu Chen ; Angel X. Chang ; Matthias Nießner
COMMENTS:       Project page: https://daveredrum.github.io/ScanRefer/
HIGHLIGHT:      We introduce the task of 3D object localization in RGB-D scans using natural language descriptions. We also introduce the ScanRefer dataset, containing 51,583 descriptions of 11,046 objects from 800 ScanNet scenes.

58, TITLE:      MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts
http://arxiv.org/abs/2007.03995
AUTHORS:        Nabeel Seedat
COMMENTS:       4 pages, 4 figures, Accepted to KDD 2020 - Applied Data Science for Healthcare Workshop (Spotlight presentation)
HIGHLIGHT:      Thus, we present a framework of uncertainty representation evaluated for medical image segmentation, using MCU-Net which combines a U-Net with Monte Carlo Dropout, evaluated with four different uncertainty metrics.

59, TITLE:      Constructive Game Logic
http://arxiv.org/abs/2002.08523
AUTHORS:        Brandon Bohrer ; André Platzer
COMMENTS:       74 pages, extended preprint for ESOP
HIGHLIGHT:      Our major contributions include: 1) a novel realizability semantics capturing the adversarial dynamics of games, 2) a natural deduction calculus and operational semantics describing the computational meaning of strategies via proof-terms, and 3) theoretical results including soundness of the proof calculus w.r.t. realizability semantics, progress and preservation of the operational semantics of proofs, and Existence Properties on support of the extraction of computational artifacts from game proofs.

60, TITLE:      Robust Autocalibrated Structured Low-Rank EPI Ghost Correction
http://arxiv.org/abs/1907.13261
AUTHORS:        Rodrigo A. Lobos ; W. Scott Hoge ; Ahsan Javed ; Congyu Liao ; Kawin Setsompop ; Krishna S. Nayak ; Justin P. Haldar
HIGHLIGHT:      Purpose: We propose and evaluate a new structured low-rank method for EPI ghost correction called Robust Autocalibrated LORAKS (RAC-LORAKS).

61, TITLE:      SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
http://arxiv.org/abs/1910.08681
AUTHORS:        Qing Guo ; Xiaofei Xie ; Felix Juefei-Xu ; Lei Ma ; Zhongguo Li ; Wanli Xue ; Wei Feng ; Yang Liu
COMMENTS:       18 pages, 5 figures. This paper has been accepted to ECCV2020
HIGHLIGHT:      In this paper, we identify a new task for the adversarial attack to visual tracking: online generating imperceptible perturbations that mislead trackers along an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA).

62, TITLE:      Interpretable Foreground Object Search As Knowledge Distillation
http://arxiv.org/abs/2007.09867
AUTHORS:        Boren Li ; Po-Yu Zhuang ; Jian Gu ; Mingyang Li ; Ping Tan
COMMENTS:       This paper will appear at ECCV 2020
HIGHLIGHT:      This paper proposes a knowledge distillation method for foreground object search (FoS).

63, TITLE:      Consciousness and Automated Reasoning
http://arxiv.org/abs/2001.09442
AUTHORS:        Ulrike Barthelmeß ; Ulrich Furbach ; Claudia Schon
HIGHLIGHT:      This paper aims at demonstrating how a first-order logic reasoning system in combination with a large knowledge base can be understood as an artificial consciousness system.

64, TITLE:      Fast Training of Deep Networks with One-Class CNNs
http://arxiv.org/abs/2007.00046
AUTHORS:        Abdul Mueed Hafiz ; Ghulam Mohiuddin Bhat
COMMENTS:       Camera Ready: 2nd International Conference on Cybernetics, Cognition and Machine Learning Applications(ICCCMLA), 2020, India
HIGHLIGHT:      The proposed approach is a viable effort in this direction.

65, TITLE:      Adaptive Offline Quintuplet Loss for Image-Text Matching
http://arxiv.org/abs/2003.03669
AUTHORS:        Tianlang Chen ; Jiajun Deng ; Jiebo Luo
COMMENTS:       Accepted by ECCV 2020. Code is available at https://github.com/sunnychencool/AOQ
HIGHLIGHT:      In this paper, we propose solutions by sampling negatives offline from the whole training set.

66, TITLE:      InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics
http://arxiv.org/abs/2004.06592
AUTHORS:        Ignacio Serna ; Alejandro Peña ; Aythami Morales ; Julian Fierrez
HIGHLIGHT:      We present a comprehensive analysis of bias effects when using an unbalanced training dataset on the features learned by the models.

67, TITLE:      MEUZZ: Smart Seed Scheduling for Hybrid Fuzzing
http://arxiv.org/abs/2002.08568
AUTHORS:        Yaohui Chen ; Mansour Ahmadi ; Reza Mirzazade farkhani ; Boyu Wang ; Long Lu
COMMENTS:       The 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID), Donostia / San Sebastian, Spain, October 2020
HIGHLIGHT:      To overcome this problem, we design a Machine learning-Enhanced hybrid fUZZing system (MEUZZ), which employs supervised machine learning for adaptive and generalizable seed scheduling.

68, TITLE:      Simulating Content Consistent Vehicle Datasets with Attribute Descent
http://arxiv.org/abs/1912.08855
AUTHORS:        Yue Yao ; Liang Zheng ; Xiaodong Yang ; Milind Naphade ; Tom Gedeon
COMMENTS:       Accepted to ECCV2020
HIGHLIGHT:      We propose an attribute descent approach to let VehicleX approximate the attributes in real-world datasets.

69, TITLE:      GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
http://arxiv.org/abs/2003.02567
AUTHORS:        Lei Kang ; Pau Riba ; Yaxing Wang ; Marçal Rusiñol ; Alicia Fornés ; Mauricio Villegas
COMMENTS:       Accepted to ECCV2020
HIGHLIGHT:      In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words.

70, TITLE:      Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations
http://arxiv.org/abs/2007.07423
AUTHORS:        Hong-Yu Zhou ; Shuang Yu ; Cheng Bian ; Yifan Hu ; Kai Ma ; Yefeng Zheng
COMMENTS:       MICCAI 2020 early accept; Code and pretrained models available at https://github.com/funnyzhou/C2L_MICCAI2020
HIGHLIGHT:      To bridge this gap, we propose a new pretraining method which learns from 700k radiographs given no manual annotations.

71, TITLE:      Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
http://arxiv.org/abs/2001.09021
AUTHORS:        Zhao Zhang ; Zemin Tang ; Yang Wang ; Zheng Zhang ; Zhengjun Zha ; Meng Wang
COMMENTS:       arXiv admin note: text overlap with arXiv:1912.07016
HIGHLIGHT:      In this paper, we mainly explore how to enhance the local and global dense feature flow by exploiting hierarchical features fully from all the convolution layers.

72, TITLE:      Geometric rank of tensors and subrank of matrix multiplication
http://arxiv.org/abs/2002.09472
AUTHORS:        Swastik Kopparty ; Guy Moshkovitz ; Jeroen Zuiddam
HIGHLIGHT:      Motivated by problems in algebraic complexity theory (e.g., matrix multiplication) and extremal combinatorics (e.g., the cap set problem and the sunflower problem), we introduce the geometric rank as a new tool in the study of tensors and hypergraphs.