2020.07.16.txt

==========New Papers==========
1, TITLE:       P2D: a self-supervised method for depth estimation from polarimetry
http://arxiv.org/abs/2007.07567
AUTHORS:        Marc Blanchon ; Désiré Sidibé ; Olivier Morel ; Ralph Seulin ; Daniel Braun ; Fabrice Meriaudeau
COMMENTS:       8 pages, submitted to ICPR2020 second round
HIGHLIGHT:      In response to this phenomenon, we propose using polarimetry as an input for a self-supervised monodepth network.

2, TITLE:       Learning Part Boundaries from 3D Point Clouds
http://arxiv.org/abs/2007.07563
AUTHORS:        Marios Loizou ; Melinos Averkiou ; Evangelos Kalogerakis
COMMENTS:       Appeared in Eurographics Symposium on Geometry Processing 2020
HIGHLIGHT:      We present a method that detects boundaries of parts in 3D shapes represented as point clouds.

3, TITLE:       Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks
http://arxiv.org/abs/2007.07562
AUTHORS:        Pavel Blinov ; Manvel Avetisian ; Vladimir Kokh ; Dmitry Umerenkov ; Alexander Tuzhilin
COMMENTS:       To be published in the proceedings of 2020 International Conference on Artificial Intelligence in Medicine, Minneapolis MN, USA
HIGHLIGHT:      In this paper we study the problem of predicting clinical diagnoses from textual Electronic Health Records (EHR) data.

4, TITLE:       Learning Syllogism with Euler Neural-Networks
http://arxiv.org/abs/2007.07320
AUTHORS:        Tiansi Dong ; Chengjiang Li ; Christian Bauckhage ; Juanzi Li ; Stefan Wrobel ; Armin B. Cremers
COMMENTS:       14 pages, 4 figures
HIGHLIGHT:      As basic logic relations are better represented by topological relations between regions, we propose a novel neural network that represents everything as a ball and is able to learn topological configuration as an Euler diagram.

5, TITLE:       Deep learning models for representing out-of-vocabulary words
http://arxiv.org/abs/2007.07318
AUTHORS:        Johannes V. Lochter ; Renato M. Silva ; Tiago A. Almeida
COMMENTS:       Preprint of the paper accepted to presentation at the 9th Brazilian Conference on Intelligent Systems (BRACIS 2020)
HIGHLIGHT:      To better understand this problem and finding the best techniques to handle OOV words, in this study, we present a comprehensive performance evaluation of deep learning models for representing OOV words.

6, TITLE:       Explicit Extremal Designs and Applications to Extractors
http://arxiv.org/abs/2007.07772
AUTHORS:        Eshan Chattopadhyay ; Jesse Goodman
COMMENTS:       18 pages
HIGHLIGHT:      For all constants $r\geq s\in\mathbb{N}$ with $r$ even, we explicitly construct $(n,r,s)$-designs $(G_n)_{n\in\mathbb{N}}$ with independence number $\alpha(G_n)\leq O(n^{\frac{2(r-s)}{r}})$.

7, TITLE:       Learning with Privileged Information for Efficient Image Super-Resolution
http://arxiv.org/abs/2007.07524
AUTHORS:        Wonkyung Lee ; Junghyup Lee ; Dohyung Kim ; Bumsub Ham
COMMENTS:       ECCV-2020
HIGHLIGHT:      We introduce in this paper a novel distillation framework, consisting of teacher and student networks, that allows to boost the performance of FSRCNN drastically.

8, TITLE:       Learning to Parse Wireframes in Images of Man-Made Environments
http://arxiv.org/abs/2007.07527
AUTHORS:        Kun Huang ; Yifan Wang ; Zihan Zhou ; Tianjiao Ding ; Shenghua Gao ; Yi Ma
COMMENTS:       CVPR 2018
HIGHLIGHT:      In this paper, we propose a learning-based approach to the task of automatically extracting a "wireframe" representation for images of cluttered man-made environments. To this end, we have built a very large new dataset of over 5,000 images with wireframes thoroughly labelled by humans.

9, TITLE:       RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition
http://arxiv.org/abs/2007.07542
AUTHORS:        Xiaoyu Yue ; Zhanghui Kuang ; Chenhao Lin ; Hongbin Sun ; Wayne Zhang
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      To suppress the side-effect of the attention drift, we propose one novel position enhancement branch, and dynamically fuse its outputs with those of the decoder attention module for scene text recognition.

10, TITLE:      Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors
http://arxiv.org/abs/2007.07396
AUTHORS:        Fredrik Svanstrom ; Cristofer Englund ; Fernando Alonso-Fernandez
HIGHLIGHT:      This paper explores the process of designing an automatic multi-sensor drone detection system.

11, TITLE:      AdapterHub: A Framework for Adapting Transformers
http://arxiv.org/abs/2007.07779
AUTHORS:        Jonas Pfeiffer ; Andreas Rücklé ; Clifton Poth ; Aishwarya Kamath ; Ivan Vulić ; Sebastian Ruder ; Kyunghyun Cho ; Iryna Gurevych
HIGHLIGHT:      We propose AdapterHub, a framework that allows dynamic "stitching-in" of pre-trained adapters for different tasks and languages.

12, TITLE:      Concept Learners for Generalizable Few-Shot Learning
http://arxiv.org/abs/2007.07375
AUTHORS:        Kaidi Cao ; Maria Brbic ; Jure Leskovec
HIGHLIGHT:      Here we propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions.

13, TITLE:      Learning Visual Context by Comparison
http://arxiv.org/abs/2007.07506
AUTHORS:        Minchul Kim ; Jongchan Park ; Seil Na ; Chang Min Park ; Donggeun Yoo
COMMENTS:       ECCV 2020 spotlight paper
HIGHLIGHT:      In this paper, we present Attend-and-Compare Module (ACM) for capturing the difference between an object of interest and its corresponding context.

14, TITLE:      Emoji Prediction: Extensions and Benchmarking
http://arxiv.org/abs/2007.07389
AUTHORS:        Weicheng Ma ; Ruibo Liu ; Lili Wang ; Soroush Vosoughi
COMMENTS:       In Proceedings of the 9th KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM 20). San Diego, CA, USA, 7 pages
HIGHLIGHT:      In this paper, we extend the existing setting of the emoji prediction task to include a richer set of emojis and to allow multi-label classification on the task. We also construct multiple emoji prediction datasets from Twitter using heuristics. We also release our datasets at https://github.com/hikari-NYU/Emoji_Prediction_Datasets_MMS for future researchers.

15, TITLE:      Monocular Retinal Depth Estimation and Joint Optic Disc and Cup Segmentation using Adversarial Networks
http://arxiv.org/abs/2007.07502
AUTHORS:        Sharath M Shankaranarayana ; Keerthi Ram ; Kaushik Mitra ; Mohanasankar Sivaprakasam
HIGHLIGHT:      To this end, we propose a novel method using adversarial network to predict depth map from a single image.

16, TITLE:      Active World Model Learning with Progress Curiosity
http://arxiv.org/abs/2007.07853
AUTHORS:        Kuno Kim ; Megumi Sano ; Julian De Freitas ; Nick Haber ; Daniel Yamins
COMMENTS:       ICML 2020. Video of results at https://bit.ly/31vg7v1
HIGHLIGHT:      In this work, we study how to design such a curiosity-driven Active World Model Learning (AWML) system.

17, TITLE:      Efficient Online Estimation of Empowerment for Reinforcement Learning
http://arxiv.org/abs/2007.07356
AUTHORS:        Ruihan Zhao ; Pieter Abbeel ; Stas Tiomkin
HIGHLIGHT:      In this work, we present an efficient online algorithm for a type of intrinsic motivation, known as empowerment, and address both limitations.

18, TITLE:      TinyVIRAT: Low-resolution Video Action Recognition
http://arxiv.org/abs/2007.07355
AUTHORS:        Ugur Demir ; Yogesh S Rawat ; Mubarak Shah
HIGHLIGHT:      In this work, we focus on recognizing tiny actions in videos. We introduce a benchmark dataset, TinyVIRAT, which contains natural low-resolution activities.

19, TITLE:      Tackling the Problem of Limited Data and Annotations in Semantic Segmentation
http://arxiv.org/abs/2007.07357
AUTHORS:        Ahmadreza Jeddi
COMMENTS:       10 pages, 8 figures, 1 table
HIGHLIGHT:      In this work, the case of semantic segmentation on a small image dataset (simulated by 1000 randomly selected images from PASCAL VOC 2012), where only weak supervision signals (scribbles from user interaction) are available is studied.

20, 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; soon to be replaced with camera-ready version
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.

21, TITLE:      Few-shot Scene-adaptive Anomaly Detection
http://arxiv.org/abs/2007.07843
AUTHORS:        Yiwei Lu ; Frank Yu ; Mahesh Kumar Krishna Reddy ; Yang Wang
COMMENTS:       Accepted to ECCV 2020 as a spotlight paper
HIGHLIGHT:      In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches.

22, TITLE:      A Generalization of Otsu's Method and Minimum Error Thresholding
http://arxiv.org/abs/2007.07350
AUTHORS:        Jonathan T. Barron
COMMENTS:       ECCV 2020
HIGHLIGHT:      We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding.

23, TITLE:      Reorganizing local image features with chaotic maps: an application to texture recognition
http://arxiv.org/abs/2007.07456
AUTHORS:        Joao Florindo
HIGHLIGHT:      In this context, we propose here a chaos-based local descriptor for texture recognition.

24, TITLE:      Graph-Based Social Relation Reasoning
http://arxiv.org/abs/2007.07453
AUTHORS:        Wanhua Li ; Yueqi Duan ; Jiwen Lu ; Jianjiang Feng ; Jie Zhou
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we propose a simpler, faster, and more accurate method named graph relational reasoning network (GR2N) for social relation recognition.

25, TITLE:      Dialect Diversity in Text Summarization on Twitter
http://arxiv.org/abs/2007.07860
AUTHORS:        L. Elisa Celis ; Vijay Keswani
HIGHLIGHT:      To correct for this bias, we propose a framework that takes an existing summarization algorithm as a blackbox and, using a small set of dialect-diverse sentences, returns a summary that is relatively more dialect-diverse.

26, TITLE:      RGB-IR Cross-modality Person ReID based on Teacher-Student GAN Model
http://arxiv.org/abs/2007.07452
AUTHORS:        Ziyue Zhang ; Shuai Jiang ; Congzhentao Huang ; Yang Li ; Richard Yi Da Xu
COMMENTS:       8 pages including 1 page reference
HIGHLIGHT:      To solve this challenge, we proposed a Teacher-Student GAN model (TS-GAN) to adopt different domains and guide the ReID backbone to learn better ReID information.

27, TITLE:      SpaceNet: Make Free Space For Continual Learning
http://arxiv.org/abs/2007.07617
AUTHORS:        Ghada Sokar ; Decebal Constantin Mocanu ; Mykola Pechenizkiy
COMMENTS:       10 pages
HIGHLIGHT:      In this work, we propose a novel architectural-based method referred as SpaceNet for class incremental learning scenario where we utilize the available fixed capacity of the model intelligently.

28, TITLE:      A Multilingual Parallel Corpora Collection Effort for Indian Languages
http://arxiv.org/abs/2007.07691
AUTHORS:        Shashank Siripragada ; Jerin Philip ; Vinay P. Namboodiri ; C V Jawahar
COMMENTS:       9 pages. Accepted in LREC 2020
HIGHLIGHT:      Alongside, we report on the methods of constructing such corpora using tools enabled by recent advances in machine translation and cross-lingual retrieval using deep neural network based methods. We also provide a separate test corpus compiled from an independent online source that can be independently used for validating the performance in 10 Indian languages.

29, TITLE:      Proof of Concept: Automatic Type Recognition
http://arxiv.org/abs/2007.07690
AUTHORS:        Vincent Christlein ; Nikolaus Weichselbaumer ; Saskia Limbach ; Mathias Seuret
HIGHLIGHT:      We investigate the performance of type classification and type retrieval using a newly created dataset consisting of easy and difficult types used in early printed books.

30, TITLE:      Cross-Lingual Speaker Verification with Domain-Balanced Hard Prototype Mining and Language-Dependent Score Normalization
http://arxiv.org/abs/2007.07689
AUTHORS:        Jenthe Thienpondt ; Brecht Desplanques ; Kris Demuynck
COMMENTS:       submitted to INTERSPEECH 2020
HIGHLIGHT:      In this paper we describe the top-scoring IDLab submission for the text-independent task of the Short-duration Speaker Verification (SdSV) Challenge 2020.

31, TITLE:      Downsampling for Testing and Learning in Product Distributions
http://arxiv.org/abs/2007.07449
AUTHORS:        Nathaniel Harms ; Yuichi Yoshida
COMMENTS:       31 pages, 1 figure
HIGHLIGHT:      We study the domain reduction problem of eliminating dependence on $n$ from the complexity of property testing and learning algorithms on domain $[n]^d$, and the related problem of establishing testing and learning results for product distributions over $\mathbb{R}^d$.

32, TITLE:      Augmented Bi-path Network for Few-shot Learning
http://arxiv.org/abs/2007.07614
AUTHORS:        Baoming Yan ; Chen Zhou ; Bo Zhao ; Kan Guo ; Jiang Yang ; Xiaobo Li ; Ming Zhang ; Yizhou Wang
HIGHLIGHT:      To solve this problem, we propose Augmented Bi-path Network (ABNet) for learning to compare both global and local features on multi-scales.

33, TITLE:      A cellular automata approach to local patterns for texture recognition
http://arxiv.org/abs/2007.07462
AUTHORS:        Joao Florindo ; Konradin Metze
HIGHLIGHT:      In this context, here we propose a method for texture descriptors that combines the representation power of complex objects by cellular automata with the known effectiveness of local descriptors in texture analysis.

34, TITLE:      AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
http://arxiv.org/abs/2007.07435
AUTHORS:        Hadi M. Dolatabadi ; Sarah Erfani ; Christopher Leckie
COMMENTS:       30 pages, 12 figures, 14 tables
HIGHLIGHT:      In this paper, we introduce AdvFlow: a novel black-box adversarial attack method on image classifiers that exploits the power of normalizing flows to model the density of adversarial examples around a given target image.

35, TITLE:      End-to-end training of a two-stage neural network for defect detection
http://arxiv.org/abs/2007.07676
AUTHORS:        Jakob Božič ; Domen Tabernik ; Danijel Skočaj
HIGHLIGHT:      In this work, we introduce end-to-end training of the two-stage network together with several extensions to the training process, which reduce the amount of training time and improve the results on the surface defect detection tasks.

36, TITLE:      Logic Constrained Pointer Networks for Interpretable Textual Similarity
http://arxiv.org/abs/2007.07670
AUTHORS:        Subhadeep Maji ; Rohan Kumar ; Manish Bansal ; Kalyani Roy ; Pawan Goyal
COMMENTS:       Accepted at IJCAI 2020 Main Track. Sole copyright holder is IJCAI, all rights reserved. Available at https://www.ijcai.org/Proceedings/2020/333
HIGHLIGHT:      In this paper, we introduce a novel pointer network based model with a sentinel gating function to align constituent chunks, which are represented using BERT.

37, TITLE:      COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
http://arxiv.org/abs/2007.07431
AUTHORS:        Kuniaki Saito ; Kate Saenko ; Ming-Yu Liu
COMMENTS:       The paper will be presented at the EUROPEAN Conference on Computer Vision (ECCV) 2020
HIGHLIGHT:      To address the issue, we propose a new few-shot image translation model, \cocofunit, which computes the style embedding of the example images conditioned on the input image and a new module called the constant style bias.

38, TITLE:      Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation
http://arxiv.org/abs/2007.07841
AUTHORS:        Paul Tardy ; David Janiszek ; Yannick Estève ; Vincent Nguyen
HIGHLIGHT:      Using a bootstrapping approach, we provide pre-alignments that are corrected by human annotators, making a validation set against which we evaluate automatic models.

39, TITLE:      Relative Pose Estimation of Calibrated Cameras with Known $\mathrm{SE}(3)$ Invariants
http://arxiv.org/abs/2007.07686
AUTHORS:        Bo Li ; Evgeniy Martyushev ; Gim Hee Lee
HIGHLIGHT:      In this paper, we present a complete comprehensive study of the relative pose estimation problem for a calibrated camera constrained by known $\mathrm{SE}(3)$ invariant, which involves 5 minimal problems in total.

40, TITLE:      UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data
http://arxiv.org/abs/2007.07683
AUTHORS:        Qianhui Wu ; Zijia Lin ; Börje F. Karlsson ; Biqing Huang ; Jian-Guang Lou
COMMENTS:       This paper is accepted by IJCAI 2020. Code is available at https://github.com/microsoft/vert-papers/tree/master/papers/UniTrans
HIGHLIGHT:      In this paper we find that both method types can complement each other, in the sense that, the former can exploit context information via language-independent features but sees no task-specific information in the target language; while the latter generally generates pseudo target-language training data via translation but its exploitation of context information is weakened by inaccurate translations.

41, TITLE:      ContourRend: A Segmentation Method for Improving Contours by Rendering
http://arxiv.org/abs/2007.07437
AUTHORS:        Junwen Chen ; Yi Lu ; Yaran Chen ; Dongbin Zhao ; Zhonghua Pang
HIGHLIGHT:      In order to obtain fine contours, we propose a segmentation method named ContourRend which adopts a contour renderer to refine segmentation contours.

42, TITLE:      Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MRI
http://arxiv.org/abs/2007.07761
AUTHORS:        Siladittya Manna ; Saumik Bhattacharya ; Umapada Pal
HIGHLIGHT:      In this paper, we propose a self-supervised learning approach to learn transferable features from MRI clips by enforcing the model to learn anatomical features.

43, TITLE:      Multimodal Word Sense Disambiguation in Creative Practice
http://arxiv.org/abs/2007.07758
AUTHORS:        Manuel Ladron de Guevara ; Christopher George ; Akshat Gupta ; Daragh Byrne ; Ramesh Krishnamurti
COMMENTS:       9 pages, 5 figures, 2 tables
HIGHLIGHT:      We present a dataset, Ambiguous Descriptions of Art Images (ADARI), of contemporary workpieces, which aims to provide a foundational resource for subjective image description and multimodal word disambiguation in the context of creative practice.

44, TITLE:      Enhancing Generalized Zero-Shot Learning via Adversarial Visual-Semantic Interaction
http://arxiv.org/abs/2007.07757
AUTHORS:        Shivam Chandhok ; Vineeth N Balasubramanian
COMMENTS:       Under Submission
HIGHLIGHT:      In this work, we unify these paradigms into a single model which in addition to synthesizing image features, also utilizes the representation learning capabilities of the inference network to provide discriminative features for the final zero-shot recognition task.

45, TITLE:      Attention as Activation
http://arxiv.org/abs/2007.07729
AUTHORS:        Yimian Dai ; Stefan Oehmcke ; Yiquan Wu ; Kobus Barnard
HIGHLIGHT:      Inspired by their similarity, we propose a novel type of activation units called attentional activation~(ATAC) units as a unification of activation functions and attention mechanisms.

46, TITLE:      Dual Past and Future for Neural Machine Translation
http://arxiv.org/abs/2007.07728
AUTHORS:        Jianhao Yan ; Fandong Meng ; Jie Zhou
HIGHLIGHT:      In this paper, we present a novel dual framework that leverages both source-to-target and target-to-source NMT models to provide a more direct and accurate supervision signal for the Past and Future modules.

47, TITLE:      Focus-and-Expand: Training Guidance Through Gradual Manipulation of Input Features
http://arxiv.org/abs/2007.07723
AUTHORS:        Moab Arar ; Noa Fish ; Dani Daniel ; Evgeny Tenetov ; Ariel Shamir ; Amit Bermano
HIGHLIGHT:      We present a simple and intuitive Focus-and-eXpand (\fax) method to guide the training process of a neural network towards a specific solution.

48, TITLE:      Finding Non-Uniform Quantization Schemes usingMulti-Task Gaussian Processes
http://arxiv.org/abs/2007.07743
AUTHORS:        Marcelo Gennari do Nascimento ; Theo W. Costain ; Victor Adrian Prisacariu
COMMENTS:       Accepted for publication at ECCV 2020. Code availiable at https://code.active.vision
HIGHLIGHT:      We propose a novel method for neural network quantization that casts the neural architecture search problem as one of hyperparameter search to find non-uniform bit distributions throughout the layers of a CNN.

49, TITLE:      Adapting the Directed Grid Theorem into an FPT Algorithm
http://arxiv.org/abs/2007.07738
AUTHORS:        Victor Campos ; Raul Lopes ; Ana Karolinna Maia ; Ignasi Sau
COMMENTS:       31 pages, 9 figures
HIGHLIGHT:      In this paper, we adapt some of the steps of the proof of Kawarabayashi and Kreutzer to improve this XP algorithm into an FPT algorithm.

50, TITLE:      CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
http://arxiv.org/abs/2007.07577
AUTHORS:        Zhongdao Wang ; Jingwei Zhang ; Liang Zheng ; Yixuan Liu ; Yifan Sun ; Yali Li ; Shengjin Wang
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.

51, TITLE:      Defeasible RDFS via Rational Closure
http://arxiv.org/abs/2007.07573
AUTHORS:        Giovanni Casini ; Umberto Straccia
COMMENTS:       47 pages. Preprint version
HIGHLIGHT:      In this work, we show how to integrate RC within the triple language RDFS, which together with OWL2 are the two major standard semantic web ontology languages.

52, TITLE:      Failures of Contingent Thinking
http://arxiv.org/abs/2007.07703
AUTHORS:        Evan Piermont ; Peio Zuazo-Garin
HIGHLIGHT:      In this paper, we provide a theoretical framework to analyze an agent who misinterprets or misperceives the true decision problem she faces.

53, TITLE:      Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection
http://arxiv.org/abs/2007.07702
AUTHORS:        Lena M. Downes ; Ted J. Steiner ; Jonathan P. How
COMMENTS:       6 pages, 4 figures. This work was accepted by the 2020 American Control Conference
HIGHLIGHT:      This paper presents a system that uses a convolutional neural network (CNN) and image processing methods to track the location of a simulated spacecraft with an extended Kalman filter (EKF).

54, TITLE:      Spiking neural state machine for gait frequency entrainment in a flexible modular robot
http://arxiv.org/abs/2007.07346
AUTHORS:        Alex Spaeth ; Maryam Tebyani ; David Haussler ; Mircea Teodorescu
COMMENTS:       20 pages, 11 figures
HIGHLIGHT:      We propose a modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each.

55, TITLE:      InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training
http://arxiv.org/abs/2007.07834
AUTHORS:        Zewen Chi ; Li Dong ; Furu Wei ; Nan Yang ; Saksham Singhal ; Wenhui Wang ; Xia Song ; Xian-Ling Mao ; Heyan Huang ; Ming Zhou
COMMENTS:       11 pages
HIGHLIGHT:      In this work, we formulate cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts.

56, TITLE:      Tractable Fragments of Temporal Sequences of Topological Information
http://arxiv.org/abs/2007.07711
AUTHORS:        Quentin Cohen-Solal
HIGHLIGHT:      In this paper, we focus on qualitative temporal sequences of topological information.

57, TITLE:      PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Network
http://arxiv.org/abs/2007.07714
AUTHORS:        Qingshan Xu ; Wenbing Tao
HIGHLIGHT:      In this paper, a Pixelwise Visibility-aware multi-view Stereo Network (PVSNet) is proposed for robust dense 3D reconstruction.

58, TITLE:      A Finite Time Combinatorial Algorithm for Instantaneous Dynamic Equilibrium Flows
http://arxiv.org/abs/2007.07808
AUTHORS:        Lukas Graf und Tobias Harks
COMMENTS:       26 pages, 11 figures
HIGHLIGHT:      We analyze IDE within the Vickrey bottleneck model, where current travel times along a path consist of the physical travel times plus the sum of waiting times in all the queues along a path.

59, TITLE:      Fine-Tune Longformer for Jointly Predicting Rumor Stance and Veracity
http://arxiv.org/abs/2007.07803
AUTHORS:        Anant Khandelwal
COMMENTS:       10 pages, 2 figures, 6 tables; Submitted to CoDS-COMAD 2021
HIGHLIGHT:      In this paper,we propose a multi-task learning framework for jointly predicting rumor stance and veracity on the dataset released at SemEval 2019 RumorEval: Determining rumor veracity and support for rumors(SemEval 2019 Task 7), which includes social media rumors stem from a variety of breaking news stories from Reddit as well as Twit-ter.

60, TITLE:      Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and Ensemble
http://arxiv.org/abs/2007.07805
AUTHORS:        Byeongjo Kim ; Chanran Kim ; Jaehoon Lee ; Jein Song ; Gyoungsoo Park
COMMENTS:       7 pages, 2 figures, technical report of 1st Visual Inductive Priors for Data-Efficient Deep Learning Workshop Challenge in ECCV 2020
HIGHLIGHT:      We applied some techniques in three aspects: data, loss function, and prediction to enable training from scratch with less data.

61, TITLE:      VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams
http://arxiv.org/abs/2007.07817
AUTHORS:        Piyush Yadav ; Edward Curry
COMMENTS:       10 pages, 19 figures, Paper published in IEEE BigData 2019
HIGHLIGHT:      In this work, we focus on a CEP framework where users can define high-level expressive queries over videos to detect a range of spatiotemporal event patterns.

62, TITLE:      Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors
http://arxiv.org/abs/2007.07793
AUTHORS:        Aditya M. Deshpande ; Rumit Kumar ; Ali A. Minai ; Manish Kumar
COMMENTS:       10 pages, 8 figures, Accepted in Dynamic Systems and Control Conference (https://event.asme.org/DSCC)
HIGHLIGHT:      In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter with thrust vectoring capabilities.

63, TITLE:      Evaluation of Neural Network Classification Systems on Document Stream
http://arxiv.org/abs/2007.07547
AUTHORS:        Joris Voerman ; Aurelie Joseph ; Mickael Coustaty ; Vincent Poulain d Andecy ; Jean-Marc Ogier
COMMENTS:       15 pages, 3 figures and submitted to DAS conferences 2020
HIGHLIGHT:      In this paper, we analyse the efficiency of NN-based document classification systems in a sub-optimal training case, based on the situation of a company document stream.

64, TITLE:      CANet: Context Aware Network for 3D Brain Tumor Segmentation
http://arxiv.org/abs/2007.07788
AUTHORS:        Zhihua Liu ; Lei Tong ; Long Chen ; Feixiang Zhou ; Zheheng Jiang ; Qianni Zhang ; Yinhai Wang ; Caifeng Shan ; Ling Li ; Huiyu Zhou
HIGHLIGHT:      In this work, we propose a novel Context-Aware Network (CANet) with a Hybrid Context Aware Feature Extractor (HCA-FE) and a Context Guided Attentive Conditional Random Field (CG-ACRF) for feature fusion.

65, TITLE:      Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances
http://arxiv.org/abs/2007.07549
AUTHORS:        Jens Brunk ; Matthias Stierle ; Leon Papke ; Kate Revoredo ; Martin Matzner ; Jörg Becker
HIGHLIGHT:      We leverage previous work on probabilistic models to develop a Dynamic Bayesian Network technique.

66, TITLE:      Relaxed-Responsibility Hierarchical Discrete VAEs
http://arxiv.org/abs/2007.07307
AUTHORS:        Matthew Willetts ; Xenia Miscouridou ; Stephen Roberts ; Chris Holmes
COMMENTS:       10 Pages
HIGHLIGHT:      Leveraging insights from classical methods of inference we introduce $\textit{Relaxed-Responsibility Vector-Quantisation}$, a novel way to parameterise discrete latent variables, a refinement of relaxed Vector-Quantisation.

67, TITLE:      COBE: Contextualized Object Embeddings from Narrated Instructional Video
http://arxiv.org/abs/2007.07306
AUTHORS:        Gedas Bertasius ; Lorenzo Torresani
HIGHLIGHT:      Instead of relying on manually-labeled data for this task, we propose a new framework for learning Contextualized OBject Embeddings (COBE) from automatically-transcribed narrations of instructional videos.

68, TITLE:      Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks
http://arxiv.org/abs/2007.07404
AUTHORS:        Mahmoud Saeedimoghaddam ; T. F. Stepinski
COMMENTS:       23 pages, 8 figures
HIGHLIGHT:      In this paper, we employed the standard paradigm of using deep convolutional neural network for object detection task named region-based CNN for automatically identifying road intersections in scanned historical USGS maps of several U.S. cities.

69, TITLE:      Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners
http://arxiv.org/abs/2007.07403
AUTHORS:        Avisha Das ; Rakesh M. Verma
COMMENTS:       Accepted for Publication at Computaci\'on y Sistemas (CyS); Poster at CiCLing 2019 and WiML@ICML 2020
HIGHLIGHT:      The method used leverages a hierarchical deep neural model which uses a learned representation of the sentences in the input document to generate structured written emails.

70, TITLE:      Learning to Learn with Variational Information Bottleneck for Domain Generalization
http://arxiv.org/abs/2007.07645
AUTHORS:        Yingjun Du ; Jun Xu ; Huan Xiong ; Qiang Qiu ; Xiantong Zhen ; Cees G. M. Snoek ; Ling Shao
COMMENTS:       15 pages, 4 figures, ECCV2020
HIGHLIGHT:      In this paper, we address both problems.

71, 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
HIGHLIGHT:      To bridge this gap, we propose a new pretraining method which learns from 700k radiographs given no manual annotations.

72, TITLE:      Using Holographically Compressed Embeddings in Question Answering
http://arxiv.org/abs/2007.07287
AUTHORS:        Salvador E. Barbosa
COMMENTS:       12 pages, 6 figures, 1 table, 9th International Conference on Advanced Information Technologies and Applications (ICAITA 2020), July 11~12, 2020, Toronto, Canada, Advanced Natural Language Processing Sub-Conference (AdNLP 2020)
HIGHLIGHT:      This research employs holographic compression of pre-trained embeddings, to represent a token, its part-of-speech, and named entity type, in the same dimension as representing only the token.

73, TITLE:      Automatic Image Labelling at Pixel Level
http://arxiv.org/abs/2007.07415
AUTHORS:        Xiang Zhang ; Wei Zhang ; Jinye Peng ; Janping Fan
HIGHLIGHT:      To alleviate the burden of manual image labelling, we propose an interesting learning approach to generate pixel-level image labellings automatically.

74, TITLE:      Learning Multiplicative Interactions with Bayesian Neural Networks for Visual-Inertial Odometry
http://arxiv.org/abs/2007.07630
AUTHORS:        Kashmira Shinde ; Jongseok Lee ; Matthias Humt ; Aydin Sezgin ; Rudolph Triebel
COMMENTS:       Published at Workshop on AI for Autonomous Driving (AIAD), the 37th International Conference on Machine Learning, Vienna, Austria, 2020
HIGHLIGHT:      This paper presents an end-to-end multi-modal learning approach for monocular Visual-Inertial Odometry (VIO), which is specifically designed to exploit sensor complementarity in the light of sensor degradation scenarios.

75, TITLE:      AdaptiveReID: Adaptive L2 Regularization in Person Re-Identification
http://arxiv.org/abs/2007.07875
AUTHORS:        Xingyang Ni ; Liang Fang ; Heikki Huttunen
COMMENTS:       First Version
HIGHLIGHT:      We introduce an adaptive L2 regularization mechanism termed AdaptiveReID, in the setting of person re-identification.

76, TITLE:      Observations on Symmetric Circuits
http://arxiv.org/abs/2007.07496
AUTHORS:        Christian Engels
HIGHLIGHT:      We study symmetric arithmetic circuits and improve on lower bounds given by Dawar and Wilsenach (ArXiv 2020).

77, TITLE:      Visualizing Transfer Learning
http://arxiv.org/abs/2007.07628
AUTHORS:        Róbert Szabó ; Dániel Katona ; Márton Csillag ; Adrián Csiszárik ; Dániel Varga
COMMENTS:       2020 ICML Workshop on Human Interpretability in Machine Learning (WHI 2020)
HIGHLIGHT:      We provide visualizations of individual neurons of a deep image recognition network during the temporal process of transfer learning.

78, TITLE:      Fast and Robust Iterative Closet Point
http://arxiv.org/abs/2007.07627
AUTHORS:        Juyong Zhang ; Yuxin Yao ; Bailin Deng
HIGHLIGHT:      In this paper, we propose a new method for robust registration with fast convergence.

79, TITLE:      Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons
http://arxiv.org/abs/2007.07869
AUTHORS:        Paul Micaelli ; Amos Storkey
HIGHLIGHT:      In this work, we enable non-greediness over long horizons with a two-fold solution.

80, TITLE:      Deep Learning for Abstract Argumentation Semantics
http://arxiv.org/abs/2007.07629
AUTHORS:        Dennis Craandijk ; Floris Bex
COMMENTS:       Accepted at the main track of IJCAI 2020. SOLE copyright holder is IJCAI (international Joint Conferences on Artificial Intelligence)
HIGHLIGHT:      In this paper, we present a learning-based approach to determining acceptance of arguments under several abstract argumentation semantics.

81, TITLE:      Explore and Explain: Self-supervised Navigation and Recounting
http://arxiv.org/abs/2007.07268
AUTHORS:        Roberto Bigazzi ; Federico Landi ; Marcella Cornia ; Silvia Cascianelli ; Lorenzo Baraldi ; Rita Cucchiara
COMMENTS:       ICPR 2020
HIGHLIGHT:      In this paper, we devise a novel embodied setting in which an agent needs to explore a previously unknown environment while recounting what it sees during the path.

82, TITLE:      Temporal Distinct Representation Learning for Action Recognition
http://arxiv.org/abs/2007.07626
AUTHORS:        Junwu Weng ; Donghao Luo ; Yabiao Wang ; Ying Tai ; Chengjie Wang ; Jilin Li ; Feiyue Huang ; Xudong Jiang ; Junsong Yuan
COMMENTS:       16 pages, 4 figures, 7 tables
HIGHLIGHT:      In this paper, we attempt to tackle this issue through two ways.

83, TITLE:      Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation
http://arxiv.org/abs/2007.07867
AUTHORS:        Aamir Mustafa ; Rafal K. Mantiuk
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      We propose Transformation Consistency Regularization, which delves into a more challenging setting of image-to-image translation, which remains unexplored by semi-supervised algorithms.

84, TITLE:      Sinhala Language Corpora and Stopwords from a Decade of Sri Lankan Facebook
http://arxiv.org/abs/2007.07884
AUTHORS:        Yudhanjaya Wijeratne ; Nisansa de Silva
COMMENTS:       10 pages; Github repo of data linked in summary
HIGHLIGHT:      This paper presents two colloquial Sinhala language corpora from the language efforts of the Data, Analysis and Policy team of LIRNEasia, as well as a list of algorithmically derived stopwords.

85, TITLE:      Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
http://arxiv.org/abs/2007.07400
AUTHORS:        Vinay V. Ramasesh ; Ethan Dyer ; Maithra Raghu
HIGHLIGHT:      In this paper, we address this important knowledge gap, investigating how forgetting affects representations in neural network models.

86, TITLE:      Explaining Deep Neural Networks using Unsupervised Clustering
http://arxiv.org/abs/2007.07477
AUTHORS:        Sercan O. Arik ; Yu-han Liu
HIGHLIGHT:      We propose a novel method to explain trained deep neural networks (DNNs), by distilling them into surrogate models using unsupervised clustering.

87, TITLE:      Privacy Preserving Text Recognition with Gradient-Boosting for Federated Learning
http://arxiv.org/abs/2007.07296
AUTHORS:        Hanchi Ren ; Jingjing Deng ; Xianghua Xie
COMMENTS:       The paper has been submitted to BMVC2020 on April 30th
HIGHLIGHT:      We propose a novel boosting algorithm for FL to address this generalisation issue, as well as achieving much faster convergence in gradient based optimization.

88, TITLE:      Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
http://arxiv.org/abs/2007.07298
AUTHORS:        Shauharda Khadka ; Estelle Aflalo ; Mattias Marder ; Avrech Ben-David ; Santiago Miret ; Hanlin Tang ; Shie Mannor ; Tamir Hazan ; Somdeb Majumdar
HIGHLIGHT:      In this paper, we look at a complimentary approach of optimizing how tensors are mapped to on-chip memory in an inference accelerator while leaving the network parameters untouched.

89, TITLE:      Generic Outlier Detection in Multi-Armed Bandit
http://arxiv.org/abs/2007.07293
AUTHORS:        Yikun Ban ; Jingrui He
COMMENTS:       Published in SIGKDD 2020
HIGHLIGHT:      In this paper, we study the problem of outlier arm detection in multi-armed bandit settings, which finds plenty of applications in many high-impact domains such as finance, healthcare, and online advertising.

90, TITLE:      Decoding CNN based Object Classifier Using Visualization
http://arxiv.org/abs/2007.07482
AUTHORS:        Abhishek Mukhopadhyay ; Imon Mukherjee ; Pradipta Biswas
COMMENTS:       Accepted at ACM International conference on Automotive User Interface 2020
HIGHLIGHT:      This paper investigates how working of Convolutional Neural Network (CNN) can be explained through visualization in the context of machine perception of autonomous vehicles.

91, TITLE:      Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform
http://arxiv.org/abs/2007.07455
AUTHORS:        Jimin Wang ; Yingjie Hu
HIGHLIGHT:      , namely have we achieved high enough performances to possibly consider the problem of geoparsing as solved?

92, TITLE:      P$^{2}$Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation
http://arxiv.org/abs/2007.07696
AUTHORS:        Zehao Yu ; Lei Jin ; Shenghua Gao
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      In this paper, we argue that the poor performance suffers from the non-discriminative point-based matching.



==========Updates to Previous Papers==========
1, TITLE:       A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection
http://arxiv.org/abs/2007.06811
AUTHORS:        Xiaoqi Zhao ; Lihe Zhang ; Youwei Pang ; Huchuan Lu ; Lei Zhang
COMMENTS:       Accepted in ECCV2020. Code: https://github.com/Xiaoqi-Zhao-DLUT/DANet-RGBD-Saliency
HIGHLIGHT:      In this work, we design a single stream network to directly use the depth map to guide early fusion and middle fusion between RGB and depth, which saves the feature encoder of the depth stream and achieves a lightweight and real-time model.

2, TITLE:       Closed-Form Factorization of Latent Semantics in GANs
http://arxiv.org/abs/2007.06600
AUTHORS:        Yujun Shen ; Bolei Zhou
COMMENTS:       11 pages, 8 figures, 3 tables
HIGHLIGHT:      In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner.

3, TITLE:       GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
http://arxiv.org/abs/1908.00059
AUTHORS:        Yu Chen ; Lingfei Wu ; Mohammed J. Zaki
COMMENTS:       7 pages. Accepted by IJCAI 2020. Final Version. The SOLE copyright holder is IJCAI (https://www.ijcai.org), all rights reserved
HIGHLIGHT:      The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks.

4, TITLE:       Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation
http://arxiv.org/abs/2006.12285
AUTHORS:        Diyuan Lu ; Nenad Polomac ; Iskra Gacheva ; Elke Hattingen ; Jochen Triesch
COMMENTS:       Submitted to IEEE Transactions on Neural Networks and Learning Systems
HIGHLIGHT:      To overcome these challenges, we propose a new method for training a deep neural network that distills particularly representative training examples and augments the training data by mixing these samples from one class with those from the same and other classes to create additional training samples.

5, TITLE:       Neural Knowledge Extraction From Cloud Service Incidents
http://arxiv.org/abs/2007.05505
AUTHORS:        Manish Shetty ; Chetan Bansal ; Sumit Kumar ; Nikitha Rao ; Nachiappan Nagappan ; Thomas Zimmermann
HIGHLIGHT:      In this work, we address the fundamental problem of structured knowledge extraction from service incidents.

6, TITLE:       A-MAL: Automatic Movement Assessment Learning from Properly Performed Movements in 3D Skeleton Videos
http://arxiv.org/abs/1907.10004
AUTHORS:        Tal Hakim ; Ilan Shimshoni
COMMENTS:       Accepted and orally presented in CVPM 2019
HIGHLIGHT:      In this work, we introduce A-MAL, an automatic, strong movement assessment learning algorithm that only learns from properly-performed movement videos without further annotations, powered by a deviation time-segmentation algorithm, a parameter relevance detection algorithm, a novel time-warping algorithm that is based on automatic detection of common temporal points-of-interest and a textual-feedback generation mechanism.

7, TITLE:       Curriculum DeepSDF
http://arxiv.org/abs/2003.08593
AUTHORS:        Yueqi Duan ; Haidong Zhu ; He Wang ; Li Yi ; Ram Nevatia ; Leonidas J. Guibas
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we design a "shape curriculum" for learning continuous Signed Distance Function (SDF) on shapes, namely Curriculum DeepSDF.

8, TITLE:       Knowledge Hypergraphs: Prediction Beyond Binary Relations
http://arxiv.org/abs/1906.00137
AUTHORS:        Bahare Fatemi ; Perouz Taslakian ; David Vazquez ; David Poole
HIGHLIGHT:      In this work, we address the question of link prediction in knowledge hypergraphs where relations are defined on any number of entities. We also develop public datasets, benchmarks and baselines for hypergraph prediction and show experimentally that the proposed models are more effective than the baselines.

9, TITLE:       Estimating People Flows to Better Count Them in Crowded Scenes
http://arxiv.org/abs/1911.10782
AUTHORS:        Weizhe Liu ; Mathieu Salzmann ; Pascal Fua
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing.

10, TITLE:      JNR: Joint-based Neural Rig Representation for Compact 3D Face Modeling
http://arxiv.org/abs/2007.06755
AUTHORS:        Noranart Vesdapunt ; Mitch Rundle ; HsiangTao Wu ; Baoyuan Wang
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we introduce a novel approach to learn a 3D face model using a joint-based face rig and a neural skinning network.

11, TITLE:      CenterNet3D:An Anchor free Object Detector for Autonomous Driving
http://arxiv.org/abs/2007.07214
AUTHORS:        Guojun Wang ; Bin Tian ; Yunfeng Ai ; Tong Xu ; Long Chen ; Dongpu Cao
COMMENTS:       9 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:1912.12791 by other authors
HIGHLIGHT:      In this paper, we eliminate anchors and model an object as a single point the center point of its bounding box.

12, TITLE:      Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
http://arxiv.org/abs/2007.04082
AUTHORS:        Lakshay Chauhan ; John Alberg ; Zachary C. Lipton
HIGHLIGHT:      We propose lookahead factor models which plug these predicted future fundamentals into traditional factors.

13, TITLE:      DeepSinger: Singing Voice Synthesis with Data Mined From the Web
http://arxiv.org/abs/2007.04590
AUTHORS:        Yi Ren ; Xu Tan ; Tao Qin ; Jian Luan ; Zhou Zhao ; Tie-Yan Liu
COMMENTS:       Accepted by KDD2020 research track
HIGHLIGHT:      In this paper, we develop DeepSinger, a multi-lingual multi-singer singing voice synthesis (SVS) system, which is built from scratch using singing training data mined from music websites.

14, TITLE:      Toward XAI for Intelligent Tutoring Systems: A Case Study in Intelligent Tutoring Systems
http://arxiv.org/abs/1912.04464
AUTHORS:        Cristina Conati ; Oswald Barral ; Vanessa Putnam ; Lea Rieger
HIGHLIGHT:      We present the design of the explanation functionality and the results of a controlled study to evaluate its impact on students' learning and perception of the ACPS hints.

15, TITLE:      Train Your Data Processor: Distribution-Aware and Error-Compensation Coordinate Decoding for Human Pose Estimation
http://arxiv.org/abs/2007.05887
AUTHORS:        Feiyu Yang ; Yu Chen ; Zhe Pan ; Min Zhang ; Min Xue ; Yaoyang Mo ; Yao Zhang ; Guoxiong Guan ; Beibei Qian ; Zhenzhong Xiao ; Zhan Song
COMMENTS:       Improve the state-of-the-art of COCO keypoint detection challenge by 1-2 AP. Project page: https://github.com/fyang235/DAEC
HIGHLIGHT:      Serving as a model-agnostic plug-in, DAEC learns its decoding strategy from training data and remarkably improves the performance of a variety of state-of-the-art human pose estimation models with negligible extra computation.

16, TITLE:      Improving Face Recognition by Clustering Unlabeled Faces in the Wild
http://arxiv.org/abs/2007.06995
AUTHORS:        Aruni RoyChowdhury ; Xiang Yu ; Kihyuk Sohn ; Erik Learned-Miller ; Manmohan Chandraker
COMMENTS:       ECCV 2020
HIGHLIGHT:      To address this, we propose a novel identity separation method based on extreme value theory.

17, TITLE:      Multiplicative Up-Drift
http://arxiv.org/abs/1904.05682
AUTHORS:        Benjamin Doerr ; Timo Kötzing
COMMENTS:       Significantly extended version of: Benjamin Doerr and Timo K\"otzing. Multiplicative up-drift. In Genetic and Evolutionary Computation Conference, GECCO 2019, pages 1470-1478. ACM, 2019
HIGHLIGHT:      Motivated by questions like how fast fit individuals take over a population, we analyze random processes exhibiting a $(1+\delta)$-multiplicative growth in expectation.

18, TITLE:      Reinforcement Learning Under Moral Uncertainty
http://arxiv.org/abs/2006.04734
AUTHORS:        Adrien Ecoffet ; Joel Lehman
COMMENTS:       33 pages, 17 figures; update adds discussion of a possible flaw of Nash voting, discussion of further possible research into MEC, as well as a few more references
HIGHLIGHT:      Inspired by such work, this paper proposes a formalism that translates such insights to the field of reinforcement learning.

19, TITLE:      Tabletop Roleplaying Games as Procedural Content Generators
http://arxiv.org/abs/2007.06108
AUTHORS:        Matthew Guzdial ; Devi Acharya ; Max Kreminski ; Michael Cook ; Mirjam Eladhari ; Antonios Liapis ; Anne Sullivan
COMMENTS:       9 pages, 2 figures, FDG Workshop on Procedural Content Generation 2020
HIGHLIGHT:      In this paper, we argue that TTRPG design can usefully be viewed as procedural content generator design.

20, TITLE:      Disentangling the Spatial Structure and Style in Conditional VAE
http://arxiv.org/abs/1910.13062
AUTHORS:        Ziye Zhang ; Li Sun ; Zhilin Zheng ; Qingli Li
COMMENTS:       5 pages, 3 figures
HIGHLIGHT:      This paper aims to disentangle the latent space in cVAE into the spatial structure and the style code, which are complementary to each other, with one of them $z_s$ being label relevant and the other $z_u$ irrelevant.

21, TITLE:      Path Signatures on Lie Groups
http://arxiv.org/abs/2007.06633
AUTHORS:        Darrick Lee ; Robert Ghrist
COMMENTS:       64 pages, 11 figures
HIGHLIGHT:      To demonstrate universality, we analyze the human action recognition problem in computer vision, using $SO(3)$ representations for the time series, providing comparable performance to other shallow learning approaches, while offering an easily interpretable feature set.

22, TITLE:      A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images
http://arxiv.org/abs/2004.07965
AUTHORS:        Pradeeban Kathiravelu ; Puneet Sharma ; Ashish Sharma ; Imon Banerjee ; Hari Trivedi ; Saptarshi Purkayastha ; Priyanshu Sinha ; Alexandre Cadrin-Chenevert ; Nabile Safdar ; Judy Wawira Gichoya
COMMENTS:       Preprint
HIGHLIGHT:      We propose Niffler, an integrated framework that enables the execution of ML pipelines at research clusters by efficiently querying and retrieving radiology images from the Picture Archiving and Communication Systems (PACS) of the hospitals.

23, TITLE:      Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
http://arxiv.org/abs/2004.07703
AUTHORS:        Fei Pan ; Inkyu Shin ; Francois Rameau ; Seokju Lee ; In So Kweon
COMMENTS:       Accepted to CVPR 2020 as an Oral Presentation. Code is available at https://github.com/feipan664/IntraDA
HIGHLIGHT:      In this work, we propose a two-step self-supervised domain adaptation approach to minimize the inter-domain and intra-domain gap together.

24, TITLE:      Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search
http://arxiv.org/abs/1911.12126
AUTHORS:        Xiangxiang Chu ; Tianbao Zhou ; Bo Zhang ; Jixiang Li
COMMENTS:       Accepted to ECCV 2020, camera ready version
HIGHLIGHT:      Thereby, we present a novel approach called Fair DARTS where the exclusive competition is relaxed to be collaborative.

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

26, TITLE:      CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
http://arxiv.org/abs/1904.02141
AUTHORS:        Yuying Zhu ; Guoxin Wang ; Börje F. Karlsson
COMMENTS:       This paper is accepted by NAACL-HLT 2019. The code is available at https://github.com/microsoft/vert-papers/tree/master/papers/CAN-NER
HIGHLIGHT:      In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts.

27, TITLE:      Inspector Gadget: A Data Programming-based Labeling System for Industrial Images
http://arxiv.org/abs/2004.03264
AUTHORS:        Geon Heo ; Yuji Roh ; Seonghyeon Hwang ; Dayun Lee ; Steven Euijong Whang
COMMENTS:       10 pages, 11 figures
HIGHLIGHT:      In this work, we expand the horizon of data programming by directly applying it to images without this conversion, which is a common scenario for industrial applications.

28, TITLE:      Feature Quantization Improves GAN Training
http://arxiv.org/abs/2004.02088
AUTHORS:        Yang Zhao ; Chunyuan Li ; Ping Yu ; Jianfeng Gao ; Changyou Chen
COMMENTS:       The first two authors contributed equally to this manuscript. ICML 2020. Code: https://github.com/YangNaruto/FQ-GAN
HIGHLIGHT:      In this work, we propose Feature Quantization (FQ) for the discriminator, to embed both true and fake data samples into a shared discrete space.

29, TITLE:      Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions
http://arxiv.org/abs/1912.00070
AUTHORS:        Vishwanath A. Sindagi ; Poojan Oza ; Rajeev Yasarla ; Vishal M. Patel
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      To address this issue, we propose an unsupervised prior-based domain adversarial object detection framework for adapting the detectors to hazy and rainy conditions.

30, TITLE:      Progressive Cluster Purification for Unsupervised Feature Learning
http://arxiv.org/abs/2007.02577
AUTHORS:        Yifei Zhang ; Chang Liu ; Yu Zhou ; Wei Wang ; Weiping Wang ; Qixiang Ye
COMMENTS:       8 pages, 5 figures
HIGHLIGHT:      In this work, we propose a novel clustering based method, which, by iteratively excluding class inconsistent samples during progressive cluster formation, alleviates the impact of noise samples in a simple-yet-effective manner.

31, TITLE:      Pretraining Image Encoders without Reconstruction via Feature Prediction Loss
http://arxiv.org/abs/2003.07441
AUTHORS:        Gustav Grund Pihlgren ; Fredrik Sandin ; Marcus Liwicki
HIGHLIGHT:      The method implementation used in this work is available online: https://github.com/guspih/Perceptual-Autoencoders

32, TITLE:      Structured Landmark Detection via Topology-Adapting Deep Graph Learning
http://arxiv.org/abs/2004.08190
AUTHORS:        Weijian Li ; Yuhang Lu ; Kang Zheng ; Haofu Liao ; Chihung Lin ; Jiebo Luo ; Chi-Tung Cheng ; Jing Xiao ; Le Lu ; Chang-Fu Kuo ; Shun Miao
COMMENTS:       Accepted to ECCV-20. Camera-ready with supplementary material
HIGHLIGHT:      In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection.

33, TITLE:      Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
http://arxiv.org/abs/2007.05230
AUTHORS:        Jing Yao ; Danfeng Hong ; Jocelyn Chanussot ; Deyu Meng ; Xiaoxiang Zhu ; Zongben Xu
HIGHLIGHT:      To this end, we propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet for short, to enhance the spatial resolution of HSI by means of higher-spatial-resolution multispectral image (MSI).

34, TITLE:      Captioning Images Taken by People Who Are Blind
http://arxiv.org/abs/2002.08565
AUTHORS:        Danna Gurari ; Yinan Zhao ; Meng Zhang ; Nilavra Bhattacharya
HIGHLIGHT:      Observing that people who are blind have relied on (human-based) image captioning services to learn about images they take for nearly a decade, we introduce the first image captioning dataset to represent this real use case.

35, TITLE:      Modular Representation Underlies Systematic Generalization in Neural Natural Language Inference Models
http://arxiv.org/abs/2004.14623
AUTHORS:        Atticus Geiger ; Kyle Richardson ; Christopher Potts
HIGHLIGHT:      In adversarial testing, we pose hard generalization tasks in order to gain insights into the solutions found by our models.

36, TITLE:      Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language
http://arxiv.org/abs/2004.12440
AUTHORS:        Qianhui Wu ; Zijia Lin ; Börje F. Karlsson ; Jian-Guang Lou ; Biqing Huang
COMMENTS:       This paper is accepted by ACL2020. Code is available at https://github.com/microsoft/vert-papers/tree/master/papers/SingleMulti-TS
HIGHLIGHT:      In this paper, we propose a teacher-student learning method to address such limitations, where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language.

37, TITLE:      Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New Benchmark
http://arxiv.org/abs/1904.03845
AUTHORS:        Guangrun Wang ; Guangcong Wang ; Xujie Zhang ; Jianhuang Lai ; Zhengtao Yu ; Liang Lin
COMMENTS:       Accepted by TNNLS 2020
HIGHLIGHT:      To solve this problem, we introduce a differentiable graphical model to capture the dependencies from all images in a bag and generate a reliable pseudo label for each person image.

38, TITLE:      Neural Belief Reasoner
http://arxiv.org/abs/1909.04719
AUTHORS:        Haifeng Qian
HIGHLIGHT:      This paper proposes a new generative model called neural belief reasoner (NBR).

39, TITLE:      M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
http://arxiv.org/abs/2006.10135
AUTHORS:        Tao Zhou ; Huazhu Fu ; Yu Zhang ; Changqing Zhang ; Xiankai Lu ; Jianbing Shen ; Ling Shao
COMMENTS:       Accepted by MICCAI'20
HIGHLIGHT:      To address the above issues, we propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net).

40, TITLE:      Active Crowd Counting with Limited Supervision
http://arxiv.org/abs/2007.06334
AUTHORS:        Zhen Zhao ; Miaojing Shi ; Xiaoxiao Zhao ; Li Li
COMMENTS:       ECCV2020 camera ready
HIGHLIGHT:      Active Crowd Counting with Limited Supervision

41, TITLE:      Automatic Recall Machines: Internal Replay, Continual Learning and the Brain
http://arxiv.org/abs/2006.12323
AUTHORS:        Xu Ji ; Joao Henriques ; Tinne Tuytelaars ; Andrea Vedaldi
HIGHLIGHT:      We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective, without extraneous buffers or generator networks.

42, TITLE:      End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures
http://arxiv.org/abs/1911.08460
AUTHORS:        Gabriel Synnaeve ; Qiantong Xu ; Jacob Kahn ; Tatiana Likhomanenko ; Edouard Grave ; Vineel Pratap ; Anuroop Sriram ; Vitaliy Liptchinsky ; Ronan Collobert
COMMENTS:       Published at the workshop on Self-supervision in Audio and Speech (SAS) at the 37th International Conference on Machine Learning (ICML 2020), Vienna, Austria
HIGHLIGHT:      We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functions.

43, TITLE:      Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery
http://arxiv.org/abs/1905.08789
AUTHORS:        Chaitanya Devaguptapu ; Ninad Akolekar ; Manuj M Sharma ; Vineeth N Balasubramanian
COMMENTS:       Accepted at Perception Beyond Visible Spectrum Workshop, CVPR 2019
HIGHLIGHT:      In this paper, we propose a pseudo-multimodal object detector trained on natural image domain data to help improve the performance of object detection in thermal images.

44, TITLE:      Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods
http://arxiv.org/abs/2006.00112
AUTHORS:        Weimin Zhou ; Hua Li ; Mark A. Anastasio
COMMENTS:       IEEE Transactions on Medical Imaging (Early Access), 2020
HIGHLIGHT:      In this paper, the ability of supervised learning-based methods to approximate the IO for joint signal detection and localization tasks is explored.

45, TITLE:      Guessing State Tracking for Visual Dialogue
http://arxiv.org/abs/2002.10340
AUTHORS:        Wei Pang ; Xiaojie Wang
COMMENTS:       Accepted at ECCV 2020. More details can be found at https://github.com/xubuvd/guesswhat
HIGHLIGHT:      This paper proposes a guessing state for the Guesser, and regards guess as a process with change of guessing state through a dialogue.

46, TITLE:      Resonator Networks outperform optimization methods at solving high-dimensional vector factorization
http://arxiv.org/abs/1906.11684
AUTHORS:        Spencer J. Kent ; E. Paxon Frady ; Friedrich T. Sommer ; Bruno A. Olshausen
COMMENTS:       arXiv's LaTeX compiler contains a compatibility issue with the subcaption package, screwing up the placement of Figure 6 (and subsequent figures) in V3. This update simply remedies that issue
HIGHLIGHT:      We develop theoretical foundations of Resonator Networks, a new type of recurrent neural network introduced in Frady et al. (2020) to solve a high-dimensional vector factorization problem arising in Vector Symbolic Architectures.

47, TITLE:      Adversarial Attack and Defense on Graph Data: A Survey
http://arxiv.org/abs/1812.10528
AUTHORS:        Lichao Sun ; Yingtong Dou ; Carl Yang ; Ji Wang ; Philip S. Yu ; Lifang He ; Bo Li
COMMENTS:       In submission to Journal. For more open-source and up-to-date information, please check our Github repository: https://github.com/YingtongDou/graph-adversarial-learning-literature
HIGHLIGHT:      In this work, we systemically organize the considered works based on the features of each topic.

48, TITLE:      Scalable Planning with Deep Neural Network Learned Transition Models
http://arxiv.org/abs/1904.02873
AUTHORS:        Ga Wu ; Buser Say ; Scott Sanner
COMMENTS:       36 pages
HIGHLIGHT:      In this paper, we introduce two types of nonlinear planning methods that can leverage deep neural network learned transition models: Hybrid Deep MILP Planner (HD-MILP-Plan) and Tensorflow Planner (TF-Plan).

49, TITLE:      Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal Learning
http://arxiv.org/abs/2003.08872
AUTHORS:        Liad Pollak Zuckerman ; Eyal Naor ; George Pisha ; Shai Bagon ; Michal Irani
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      In this paper we propose a "Deep Internal Learning" approach for true TSR.

50, TITLE:      Learning Delicate Local Representations for Multi-Person Pose Estimation
http://arxiv.org/abs/2003.04030
AUTHORS:        Yuanhao Cai ; Zhicheng Wang ; Zhengxiong Luo ; Binyi Yin ; Angang Du ; Haoqian Wang ; Xiangyu Zhang ; Xinyu Zhou ; Erjin Zhou ; Jian Sun
COMMENTS:       ECCV2020 Spotlight
HIGHLIGHT:      In this paper, we propose a novel method called Residual Steps Network (RSN).

51, TITLE:      Real-Time Face and Landmark Localization for Eyeblink Detection
http://arxiv.org/abs/2006.00816
AUTHORS:        Paul Bakker ; Henk-Jan Boele ; Zaid Al-Ars ; Christos Strydis
COMMENTS:       Added public gitlab repo link with paper source code
HIGHLIGHT:      In this work, a face- and landmark-detection algorithm have been carefully combined in order to provide fully automated eyelid tracking, and have further been accelerated to make the first crucial step towards online, closed-loop experiments.

52, TITLE:      Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation
http://arxiv.org/abs/2007.02424
AUTHORS:        Jiaxing Huang ; Shijian Lu ; Dayan Guan ; Xiaobing Zhang
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      This paper presents an innovative local contextual-relation consistent domain adaptation (CrCDA) technique that aims to achieve local-level consistencies during the global-level alignment.

53, TITLE:      Video Object Segmentation with Episodic Graph Memory Networks
http://arxiv.org/abs/2007.07020
AUTHORS:        Xinkai Lu ; Wenguan Wang ; Martin Danelljan ; Tianfei Zhou ; Jianbing Shen ; Luc Van Gool
COMMENTS:       ECCV2020 Spotlight; website: https://github.com/carrierlxk/GraphMemVOS
HIGHLIGHT:      In this work, a graph memory network is developed to address the novel idea of "learning to update the segmentation model".

54, TITLE:      Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration
http://arxiv.org/abs/1910.10328
AUTHORS:        Jiahao Li ; Changhao Zhang ; Ziyao Xu ; Hangning Zhou ; Chi Zhang
HIGHLIGHT:      In this paper, we propose a novel learning-based pipeline for partially overlapping 3D point cloud registration.

55, TITLE:      Confidence Regularized Self-Training
http://arxiv.org/abs/1908.09822
AUTHORS:        Yang Zou ; Zhiding Yu ; Xiaofeng Liu ; B. V. K. Vijaya Kumar ; Jinsong Wang
COMMENTS:       Accepted to ICCV 2019 (Oral)
HIGHLIGHT:      To address the problem, we propose a confidence regularized self-training (CRST) framework, formulated as regularized self-training.

56, TITLE:      Difficulty-aware Meta-learning for Rare Disease Diagnosis
http://arxiv.org/abs/1907.00354
AUTHORS:        Xiaomeng Li ; Lequan Yu ; Yueming Jin ; Chi-Wing Fu ; Lei Xing ; Pheng-Ann Heng
COMMENTS:       MICCAI2020
HIGHLIGHT:      In this paper, we present a difficulty-aware meta-learning method to address rare disease classifications and demonstrate its capability to classify dermoscopy images.

57, TITLE:      Controversial stimuli: pitting neural networks against each other as models of human recognition
http://arxiv.org/abs/1911.09288
AUTHORS:        Tal Golan ; Prashant C. Raju ; Nikolaus Kriegeskorte
HIGHLIGHT:      Here we consider the problem of comparing deep neural networks as models of human visual recognition.

58, TITLE:      Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification
http://arxiv.org/abs/2001.01536
AUTHORS:        Liuyu Xiang ; Guiguang Ding ; Jungong Han
COMMENTS:       ECCV 2020 Spotlight
HIGHLIGHT:      In this paper, we propose a novel self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME).

59, TITLE:      Inference with Choice Functions Made Practical
http://arxiv.org/abs/2005.03098
AUTHORS:        Arne Decadt ; Jasper De Bock ; Gert de Cooman
HIGHLIGHT:      We present a practical algorithm to compute this natural extension and provide several methods that can be used to improve its scalability.

60, TITLE:      Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
http://arxiv.org/abs/1911.06161
AUTHORS:        Qianhui Wu ; Zijia Lin ; Guoxin Wang ; Hui Chen ; Börje F. Karlsson ; Biqing Huang ; Chin-Yew Lin
COMMENTS:       This paper is accepted by AAAI2020. Code is available at https://github.com/microsoft/vert-papers/tree/master/papers/Meta-Cross
HIGHLIGHT:      To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo-NER tasks for meta-training by computing sentence similarities.

61, TITLE:      FastReID: A Pytorch Toolbox for General Instance Re-identification
http://arxiv.org/abs/2006.02631
AUTHORS:        Lingxiao He ; Xingyu Liao ; Wu Liu ; Xinchen Liu ; Peng Cheng ; Tao Mei
HIGHLIGHT:      To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research.

62, TITLE:      Word-level Speech Recognition with a Letter to Word Encoder
http://arxiv.org/abs/1906.04323
AUTHORS:        Ronan Collobert ; Awni Hannun ; Gabriel Synnaeve
COMMENTS:       ICML 2020
HIGHLIGHT:      We propose a direct-to-word sequence model which uses a word network to learn word embeddings from letters.

63, TITLE:      s-DRN: Stabilized Developmental Resonance Network
http://arxiv.org/abs/1912.08541
AUTHORS:        In-Ug Yoon ; Ue-Hwan Kim ;  Jong-Hwan
COMMENTS:       Under review
HIGHLIGHT:      To overcome these limitations, we propose a stabilized developmental resonance network (s-DRN).

64, TITLE:      BISM: Bytecode-Level Instrumentation for Software Monitoring
http://arxiv.org/abs/2007.03936
AUTHORS:        Chukri Soueidi ; Ali Kassem ; Yliès Falcone
HIGHLIGHT:      The language follows the aspect-oriented programming paradigm by adopting the joinpoint model, advice inlining, and separate instrumentation mechanisms.

65, TITLE:      Neural networks adapting to datasets: learning network size and topology
http://arxiv.org/abs/2006.12195
AUTHORS:        Romuald A. Janik ; Aleksandra Nowak
COMMENTS:       Fixed blank page
HIGHLIGHT:      We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a standard gradient-based training.