2020.05.28.txt

==========New Papers==========
1, TITLE:       Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection
http://arxiv.org/abs/2005.13298
AUTHORS:        Wenhao Tang ; Qiming Zhao ; Sheng Huang ; Ren Li ; Luwen Huangfu
HIGHLIGHT:      We present a novel deep learning framework named Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases not just limited to the specific ones, such as crack and pothole.

2, TITLE:       Accelerating Neural Network Inference by Overflow Aware Quantization
http://arxiv.org/abs/2005.13297
AUTHORS:        Hongwei Xie ; Shuo Zhang ; Huanghao Ding ; Yafei Song ; Baitao Shao ; Conggang Hu ; Ling Cai ; Mingyang Li
HIGHLIGHT:      To address this problem, we propose an overflow aware quantization method by designing trainable adaptive fixed-point representation, to optimize the number of bits for each input tensor while prohibiting numeric overflow during the computation.

3, TITLE:       Understanding Lua's Garbage Collection -- Towards a Formalized Static Analyzer
http://arxiv.org/abs/2005.13057
AUTHORS:        Mallku Soldevila ; Beta Ziliani ; Daniel Fridlender
COMMENTS:       Submitted for revision to PPDP 2020
HIGHLIGHT:      Our long-term goal is to provide a formalized static analyzer of Lua programs to detect potential dangers.

4, TITLE:       Multi-task deep learning for image segmentation using recursive approximation tasks
http://arxiv.org/abs/2005.13053
AUTHORS:        Rihuan Ke ; Aurélie Bugeau ; Nicolas Papadakis ; Mark Kirkland ; Peter Schuetz ; Carola-Bibiane Schönlieb
HIGHLIGHT:      In this work, we develop a multi-task learning method to relax this constraint.

5, TITLE:       Earballs: Neural Transmodal Translation
http://arxiv.org/abs/2005.13291
AUTHORS:        Andrew Port ; Chelhwon Kim ; Mitesh Patel
COMMENTS:       9 pages, 3 figures
HIGHLIGHT:      To be clear, the goal of this work is to propose a mechanism for providing an information preserving mapping that users can learn to use to see (or perceive other information) using their auditory system.

6, TITLE:       Prediction of Thrombectomy FunctionalOutcomes using Multimodal Data
http://arxiv.org/abs/2005.13061
AUTHORS:        Zeynel A. Samak ; Philip Clatworthy ; Majid Mirmehdi
COMMENTS:       Accepted at Medical Image Understanding and Analysis (MIUA) 2020
HIGHLIGHT:      We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata information, imaging data, and imaging biomarkers extracted from images) to estimate the success of endovascular treatment.

7, TITLE:       Learning with Weak Supervision for Email Intent Detection
http://arxiv.org/abs/2005.13084
AUTHORS:        Kai Shu ; Subhabrata Mukherjee ; Guoqing Zheng ; Ahmed Hassan Awadallah ; Milad Shokouhi ; Susan Dumais
COMMENTS:       10 pages, 3 figures
HIGHLIGHT:      In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails.

8, TITLE:       Benchmarking Differentially Private Residual Networks for Medical Imagery
http://arxiv.org/abs/2005.13099
AUTHORS:        Sahib Singh ; Harshvardhan Sikka
COMMENTS:       4 Pages, 2 Figures
HIGHLIGHT:      In this paper, we measure the performance of a deep neural network on differentially private image datasets pertaining to Pneumonia.

9, TITLE:       Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation
http://arxiv.org/abs/2005.13095
AUTHORS:        Nilufer Tuptuk ; Stephen Hailes
COMMENTS:       25 pages
HIGHLIGHT:      In this paper we propose a novel methodology to assist in identifying vulnerabilities in a real-world complex heterogeneous industrial control systems (ICS) using two evolutionary multiobjective optimisation (EMO) algorithms, NSGA-II and SPEA2.

10, TITLE:      A Study of Neural Matching Models for Cross-lingual IR
http://arxiv.org/abs/2005.12994
AUTHORS:        Puxuan Yu ; James Allan
COMMENTS:       4 pages, 1 figure, accepted at SIGIR'20
HIGHLIGHT:      In this study, we investigate interaction-based neural matching models for ad-hoc cross-lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs).

11, TITLE:      CausaLM: Causal Model Explanation Through Counterfactual Language Models
http://arxiv.org/abs/2005.13407
AUTHORS:        Amir Feder ; Nadav Oved ; Uri Shalit ; Roi Reichart
COMMENTS:       Our code and data are available at: \url{https://amirfeder.github.io/CausaLM/}
HIGHLIGHT:      To bridge that gap, we propose CausaLM, a framework for producing causal model explanations using counterfactual language representation models.

12, TITLE:      Neural heuristics for SAT solving
http://arxiv.org/abs/2005.13406
AUTHORS:        Sebastian Jaszczur ; Michał Łuszczyk ; Henryk Michalewski
HIGHLIGHT:      We use neural graph networks with a message-passing architecture and an attention mechanism to enhance the branching heuristic in two SAT-solving algorithms.

13, TITLE:      AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings
http://arxiv.org/abs/2005.13402
AUTHORS:        Pratik Mazumder ; Pravendra Singh ; Kranti Kumar Parida ; Vinay P. Namboodiri
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      In this paper, we solve for the problem of generalized zero-shot learning in a multi-modal setting, where we have novel classes of audio/video during testing that were not seen during training.

14, TITLE:      Thirty Musts for Meaning Banking
http://arxiv.org/abs/2005.13421
AUTHORS:        Johan Bos ; Lasha Abzianidze
COMMENTS:       https://www.aclweb.org/anthology/W19-3302/
HIGHLIGHT:      This paper lists some lessons learned in nearly ten years of meaning annotation during the development of the Groningen Meaning Bank (Bos et al., 2017) and the Parallel Meaning Bank (Abzianidze et al., 2017).

15, TITLE:      Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding
http://arxiv.org/abs/2005.13423
AUTHORS:        Yunlei Tang ; Sebastian Dorn ; Chiragkumar Savani
HIGHLIGHT:      We present Center3D, a one-stage anchor-free approach, to efficiently estimate 3D location and depth using only monocular RGB images.

16, TITLE:      Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation
http://arxiv.org/abs/2005.13201
AUTHORS:        Ashwin Raju ; Chi-Tung Cheng ; Yunakai Huo ; Jinzheng Cai ; Junzhou Huang ; Jing Xiao ; Le Lu ; ChienHuang Liao ; Adam P Harrison
COMMENTS:       23 pages, 8 figures
HIGHLIGHT:      In this work, we present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe), which only requires a small labeled cohort of single phase imaging data to adapt to any unlabeled cohort of heterogenous multi-phase data with possibly new clinical scenarios and pathologies.

17, TITLE:      Improve bone age assessment by learning from anatomical local regions
http://arxiv.org/abs/2005.13452
AUTHORS:        Dong Wang ; Kexin Zhang ; Jia Ding ; Liwei Wang
COMMENTS:       Early accepted to MICCAI2020
HIGHLIGHT:      Following the spirit of TW2, we propose a novel model called Anatomical Local-Aware Network (ALA-Net) for automatic bone age assessment.

18, TITLE:      Segmentation Loss Odyssey
http://arxiv.org/abs/2005.13449
AUTHORS:        Jun Ma
COMMENTS:       Educational Materials (https://miccai-sb.github.io/materials/Ma2019.pdf)
HIGHLIGHT:      In this paper, we present a systematic taxonomy to sort existing loss functions into four meaningful categories.

19, TITLE:      Neural Edit Completion
http://arxiv.org/abs/2005.13209
AUTHORS:        Shaked Brody ; Uri Alon ; Eran Yahav
HIGHLIGHT:      We refer to this task as the EditCompletion task and present a novel approach for tackling it.

20, TITLE:      Zoom in to the details of human-centric videos
http://arxiv.org/abs/2005.13222
AUTHORS:        Guanghan Li ; Yaping Zhao ; Mengqi Ji ; Xiaoyun Yuan ; Lu Fang
COMMENTS:       5 pages, 6 figures, accepted for presentation at IEEE ICIP 2020
HIGHLIGHT:      Zoom in to the details of human-centric videos

21, TITLE:      Arbitrary Style Transfer via Multi-Adaptation Network
http://arxiv.org/abs/2005.13219
AUTHORS:        Yingying Deng ; Fan Tang ; Weiming Dong ; Wen Sun ; Feiyue Huang ; Changsheng Xu
HIGHLIGHT:      Arbitrary Style Transfer via Multi-Adaptation Network

22, TITLE:      Self-Training for Unsupervised Parsing with PRPN
http://arxiv.org/abs/2005.13455
AUTHORS:        Anhad Mohananey ; Katharina Kann ; Samuel R. Bowman
COMMENTS:       Accepted for publication at the 16th International Conference on Parsing Technologies (IWPT), 2020
HIGHLIGHT:      In this work, we propose self-training for neural UP models: we leverage aggregated annotations predicted by copies of our model as supervision for future copies.

23, TITLE:      Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?
http://arxiv.org/abs/2005.13213
AUTHORS:        Kobi Leins ; Jey Han Lau ; Timothy Baldwin
COMMENTS:       6 pages; accepted for ACL2020
HIGHLIGHT:      We examine this question with respect to a paper on automatic legal sentencing from EMNLP 2019 which was a source of some debate, in asking whether the paper should have been allowed to be published, who should have been charged with making such a decision, and on what basis.

24, TITLE:      Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images
http://arxiv.org/abs/2005.13215
AUTHORS:        Damien Grosgeorge ; Maxime Arbelot ; Alex Goupilleau ; Tugdual Ceillier ; Renaud Allioux
HIGHLIGHT:      We present here a dedicated method to detect and identify aircraft, combining two very different convolutional neural networks (CNNs): a segmentation model, based on a modified U-net architecture, and a detection model, based on the RetinaNet architecture.

25, TITLE:      Gram filtering and sinogram interpolation for pixel-basis in parallel-beam X-ray CT reconstruction
http://arxiv.org/abs/2005.13471
AUTHORS:        Ziyu Shu ; Alireza Entezari
HIGHLIGHT:      We propose a method to improve the performance of related algorithms by calculating the Gram filter exactly and interpolating the sinogram signal optimally.

26, TITLE:      Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
http://arxiv.org/abs/2005.13485
AUTHORS:        Ruisheng Cao ; Su Zhu ; Chenyu Yang ; Chen Liu ; Rao Ma ; Yanbin Zhao ; Lu Chen ; Kai Yu
COMMENTS:       accepted by ACL 2020
HIGHLIGHT:      Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an unsupervised paraphrase model to convert an unlabeled natural language utterance into the canonical utterance.

27, TITLE:      Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3
http://arxiv.org/abs/2005.13243
AUTHORS:        Petr Hurtik ; Vojtech Molek ; Jan Hula ; Marek Vajgl ; Pavel Vlasanek ; Tomas Nejezchleba
COMMENTS:       18 pages, 15 figures, IEEE Transactions on Pattern Analysis and Machine Intelligence, Source code is available at https://gitlab.com/irafm-ai/poly-yolo
HIGHLIGHT:      We present a new version of YOLO with better performance and extended with instance segmentation called Poly-YOLO.

28, TITLE:      Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
http://arxiv.org/abs/2005.13486
AUTHORS:        Lixing Zhu ; Yulan He ; Deyu Zhou
COMMENTS:       To appear at ACL2020
HIGHLIGHT:      In this paper, we model users' tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user's historical tweet sequence and tweets posted by their neighbours.

29, TITLE:      Kernel methods library for pattern analysis and machine learning in python
http://arxiv.org/abs/2005.13483
AUTHORS:        Pradeep Reddy Raamana
COMMENTS:       6 pages, 3 code examples, 1 table
HIGHLIGHT:      Kernel methods have proven to be powerful techniques for pattern analysis and machine learning (ML) in a variety of domains.

30, TITLE:      Syntactic Structure Distillation Pretraining For Bidirectional Encoders
http://arxiv.org/abs/2005.13482
AUTHORS:        Adhiguna Kuncoro ; Lingpeng Kong ; Daniel Fried ; Dani Yogatama ; Laura Rimell ; Chris Dyer ; Phil Blunsom
COMMENTS:       17 pages, 6 tables, 2 figures. AK and LK contributed equally
HIGHLIGHT:      To answer this question, we introduce a knowledge distillation strategy for injecting syntactic biases into BERT pretraining, by distilling the syntactically informative predictions of a hierarchical---albeit harder to scale---syntactic language model.

31, TITLE:      MOPO: Model-based Offline Policy Optimization
http://arxiv.org/abs/2005.13239
AUTHORS:        Tianhe Yu ; Garrett Thomas ; Lantao Yu ; Stefano Ermon ; James Zou ; Sergey Levine ; Chelsea Finn ; Tengyu Ma
COMMENTS:       First two authors contributed equally. Last two authors advised equally
HIGHLIGHT:      In this paper, we observe that an existing model-based RL algorithm on its own already produces significant gains in the offline setting, as compared to model-free approaches, despite not being designed for this setting.

32, TITLE:      Establishing a New State-of-the-Art for French Named Entity Recognition
http://arxiv.org/abs/2005.13236
AUTHORS:        Pedro Javier Ortiz Suárez ; Yoann Dupont ; Benjamin Muller ; Laurent Romary ; Benoît Sagot
HIGHLIGHT:      We sketch the underlying annotation guidelines and we provide a few figures about the resulting annotations.

33, TITLE:      Comparing BERT against traditional machine learning text classification
http://arxiv.org/abs/2005.13012
AUTHORS:        Santiago González-Carvajal ; Eduardo C. Garrido-Merchán
COMMENTS:       12 pages
HIGHLIGHT:      Our purpose of this work is to add empirical evidence to support or refuse the use of BERT as a default on NLP tasks.

34, TITLE:      Domain-Specific Multi-Level IR Rewriting for GPU
http://arxiv.org/abs/2005.13014
AUTHORS:        Tobias Gysi ; Christoph Müller ; Oleksandr Zinenko ; Stephan Herhut ; Eddie Davis ; Tobias Wicky ; Oliver Fuhrer ; Torsten Hoefler ; Tobias Grosser
COMMENTS:       10 pages, 13 figures
HIGHLIGHT:      We demonstrate the effectiveness of this approach for the weather and climate domain.

35, TITLE:      English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too
http://arxiv.org/abs/2005.13013
AUTHORS:        Jason Phang ; Phu Mon Htut ; Yada Pruksachatkun ; Haokun Liu ; Clara Vania ; Katharina Kann ; Iacer Calixto ; Samuel R. Bowman
HIGHLIGHT:      Here, we investigate whether English intermediate-task training is still helpful on non-English target tasks in a zero-shot cross-lingual setting.

36, TITLE:      Catching Attention with Automatic Pull Quote Selection
http://arxiv.org/abs/2005.13263
AUTHORS:        Tanner Bohn ; Charles X. Ling
COMMENTS:       14 pages (11 + 3 for refs), 3 figures, 6 tables
HIGHLIGHT:      In this paper, we introduce the novel task of automatic pull quote selection, construct a dataset, and benchmark the performance of a number of approaches ranging from hand-crafted features to state-of-the-art sentence embeddings to cross-task models.

37, TITLE:      Who is this Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI
http://arxiv.org/abs/2005.13275
AUTHORS:        Irene Celino
COMMENTS:       10 pages, 1 figure, book chapter
HIGHLIGHT:      In this chapter, we claim for the need to better investigate what constitutes a human explanation, i.e. a justification of the machine behaviour that is interpretable and actionable by the human decision makers.

38, TITLE:      BRENDA: Browser Extension for Fake News Detection
http://arxiv.org/abs/2005.13270
AUTHORS:        Bjarte Botnevik ; Eirik Sakariassen ; Vinay Setty
COMMENTS:       Accepted as SIGIR demo
HIGHLIGHT:      In this demonstration, we propose BRENDA a browser extension which can be used to automate the entire process of credibility assessments of false claims.

39, TITLE:      Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition
http://arxiv.org/abs/2005.13044
AUTHORS:        Lei Kang ; Pau Riba ; Marçal Rusiñol ; Alicia Fornés ; Mauricio Villegas
HIGHLIGHT:      We propose a novel method that bypasses any recurrence.

40, TITLE:      Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection
http://arxiv.org/abs/2005.13289
AUTHORS:        Jakob Bossek ; Pascal Kerschke ; Heike Trautmann
COMMENTS:       This version has been accepted for publication at the IEEE Congress on Evolutionary Computation (IEEE CEC) 2020, which is part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020
HIGHLIGHT:      In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness.

41, TITLE:      An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images
http://arxiv.org/abs/2005.13288
AUTHORS:        Jonas Wurst ; Alberto Flores Fernández ; Michael Botsch ; Wolfgang Utschick
COMMENTS:       Copyright 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
HIGHLIGHT:      A novel unsupervised outlier score, which can be embedded into graph based dimensionality reduction techniques, is presented in this work.

42, TITLE:      OpenQL : A Portable Quantum Programming Framework for Quantum Accelerators
http://arxiv.org/abs/2005.13283
AUTHORS:        N. Khammassi ; I. Ashraf ; J. v. Someren ; R. Nane ; A. M. Krol ; M. A. Rol ; L. Lao ; K. Bertels ; C. G. Almudever
HIGHLIGHT:      In this paper, we propose a quantum programming framework named OpenQL, which includes a high-level quantum programming language and its associated quantum compiler.

43, TITLE:      Examining Racial Bias in an Online Abuse Corpus with Structural Topic Modeling
http://arxiv.org/abs/2005.13041
AUTHORS:        Thomas Davidson ; Debasmita Bhattacharya
COMMENTS:       Please cite the published version, see proceedings of ICWSM 2020
HIGHLIGHT:      We discuss how topic modeling may be a useful approach for identifying bias in annotated data.

44, TITLE:      Instance Explainable Temporal Network For Multivariate Timeseries
http://arxiv.org/abs/2005.13037
AUTHORS:        Naveen Madiraju ; Homa Karimabadi
COMMENTS:       7 pages, 7 figures, preprint
HIGHLIGHT:      Here, we propose a novel network (IETNet) that identifies the important channels in the classification decision for each instance of inference.

45, TITLE:      ALBA : Reinforcement Learning for Video Object Segmentation
http://arxiv.org/abs/2005.13039
AUTHORS:        Shreyank N Gowda ; Panagiotis Eustratiadis ; Timothy Hospedales ; Laura Sevilla-Lara
HIGHLIGHT:      We propose a network architecture for tractably performing proposal selection and joint grouping.

46, TITLE:      Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments
http://arxiv.org/abs/2005.13178
AUTHORS:        Pegah Salehi ; Abdolah Chalechale ; Maryam Taghizadeh
COMMENTS:       Submitted to a journal in the computer vision field
HIGHLIGHT:      In this paper, after introducing the main concepts and the theory of GAN, two new deep generative models are compared, the evaluation metrics utilized in the literature and challenges of GANs are also explained.

47, TITLE:      Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models
http://arxiv.org/abs/2005.13170
AUTHORS:        Haochen Liu ; Zhiwei Wang ; Tyler Derr ; Jiliang Tang
COMMENTS:       10 pages
HIGHLIGHT:      Thus, in this paper, we investigate whether we can learn to craft input sentences that result in a black-box neural dialogue model being manipulated into having its outputs contain target words or match target sentences.

48, TITLE:      Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components
http://arxiv.org/abs/2005.13186
AUTHORS:        Alex Cummaudo ; Scott Barnett ; Rajesh Vasa ; John Grundy ; Mohamed Abdelrazek
HIGHLIGHT:      Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components

49, TITLE:      Learning to segment from misaligned and partial labels
http://arxiv.org/abs/2005.13180
AUTHORS:        Simone Fobi ; Terence Conlon ; Jayant Taneja ; Vijay Modi
COMMENTS:       This is the extended version of a paper to be published in ACM COMPASS 2020
HIGHLIGHT:      In this paper, we present a novel and generalizable two-stage framework that enables improved pixel-wise image segmentation given misaligned and missing annotations.

50, TITLE:      Extrapolative-Interpolative Cycle-Consistency Learning for Video Frame Extrapolation
http://arxiv.org/abs/2005.13194
AUTHORS:        Sangjin Lee ; Hyeongmin Lee ; Taeoh Kim ; Sangyoun Lee
COMMENTS:       This paper has been accepted to 2020 IEEE International Conference on Image Processing (ICIP 2020)
HIGHLIGHT:      Unlike previous studies that usually have been focused on the design of modules or construction of networks, we propose a novel Extrapolative-Interpolative Cycle (EIC) loss using pre-trained frame interpolation module to improve extrapolation performance.

51, TITLE:      TIME: Text and Image Mutual-Translation Adversarial Networks
http://arxiv.org/abs/2005.13192
AUTHORS:        Bingchen Liu ; Kunpeng Song ; Yizhe Zhu ; Gerard de Melo ; Ahmed Elgammal
HIGHLIGHT:      Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator $G$ and an image captioning discriminator $D$ under the Generative Adversarial Network framework.

52, TITLE:      End-to-end Optimized Video Compression with MV-Residual Prediction
http://arxiv.org/abs/2005.12945
AUTHORS:        XiangJi Wu ; Ziwen Zhang ; Jie Feng ; Lei Zhou ; Junmin Wu
HIGHLIGHT:      We present an end-to-end trainable framework for P-frame compression in this paper.

53, TITLE:      Gaze-based Autism Detection for Adolescents and Young Adults using Prosaic Videos
http://arxiv.org/abs/2005.12951
AUTHORS:        Karan Ahuja ; Abhishek Bose ; Mohit Jain ; Kuntal Dey ; Anil Joshi ; Krishnaveni Achary ; Blessin Varkey ; Chris Harrison ; Mayank Goel
HIGHLIGHT:      In this short paper, we demonstrate that by monitoring a user's gaze as they watch commonplace (i.e., not specialized, structured or coded) video, we can identify individuals with autism spectrum disorder.

54, TITLE:      A comparison of Vietnamese Statistical Parametric Speech Synthesis Systems
http://arxiv.org/abs/2005.12962
AUTHORS:        Huy Kinh Phan ; Viet Lam Phung ; Tuan Anh Dinh ; Bao Quoc Nguyen
COMMENTS:       9 pages, submitted to KSE 2020
HIGHLIGHT:      In the paper, we compared four popular Vietnamese SPSS techniques using: 1) hidden Markov models (HMM), 2) deep neural networks (DNN), 3) generative adversarial networks (GAN), and 4) end-to-end (E2E) architectures, which consists of Tacontron~2 and WaveGlow vocoder in terms of speech quality and performance efficiency.

55, TITLE:      How to do Physics-based Learning
http://arxiv.org/abs/2005.13531
AUTHORS:        Michael Kellman ; Michael Lustig ; Laura Waller
COMMENTS:       3 pages, 2 figures, linked repository https://github.com/kellman/physics_based_learning
HIGHLIGHT:      The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system.

56, TITLE:      4D Visualization of Dynamic Events from Unconstrained Multi-View Videos
http://arxiv.org/abs/2005.13532
AUTHORS:        Aayush Bansal ; Minh Vo ; Yaser Sheikh ; Deva Ramanan ; Srinivasa Narasimhan
COMMENTS:       Project Page - http://www.cs.cmu.edu/~aayushb/Open4D/
HIGHLIGHT:      We present a data-driven approach for 4D space-time visualization of dynamic events from videos captured by hand-held multiple cameras.

57, TITLE:      Tracking, exploring and analyzing recent developments in German-language online press in the face of the coronavirus crisis: cOWIDplus Analysis and cOWIDplus Viewer
http://arxiv.org/abs/2005.13316
AUTHORS:        Sascha Wolfer ; Alexander Koplenig ; Frank Michaelis ; Carolin Müller-Spitzer
COMMENTS:       13 pages, 6 figures, 1 table, 3852 words
HIGHLIGHT:      We present three inter-connected resources that are designed to capture and illustrate these effects on a subset of the German language: An RSS corpus of German-language newsfeeds (with freely available untruncated unigram frequency lists), a static but continuously updated HTML page tracking the diversity of the used vocabulary and a web application that enables other researchers and the broader public to explore these effects without any or with little knowledge of corpus representation/exploration or statistical analyses.

58, TITLE:      AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
http://arxiv.org/abs/2005.13312
AUTHORS:        Xin Chen ; Yuwei Li ; Xi Luo ; Tianjia Shao ; Jingyi Yu ; Kun Zhou ; Youyi Zheng
COMMENTS:       10 pages, 12 figures
HIGHLIGHT:      This paper presents a fully automatic framework for extracting editable 3D objects directly from a single photograph.

59, TITLE:      Towards an Open Platform for Legal Information
http://arxiv.org/abs/2005.13342
AUTHORS:        Malte Ostendorff ; Till Blume ; Saskia Ostendorff
COMMENTS:       Accepted at ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2020
HIGHLIGHT:      To improve open access in the legal domain, we present our approach for an open source platform to transparently process and access Legal Open Data.

60, TITLE:      Transition-based Semantic Dependency Parsing with Pointer Networks
http://arxiv.org/abs/2005.13344
AUTHORS:        Daniel Fernández-González ; Carlos Gómez-Rodríguez
COMMENTS:       Proceedings of ACL 2020. 12 pages
HIGHLIGHT:      In order to further test the capabilities of these powerful neural networks on a harder NLP problem, we propose a transition system that, thanks to Pointer Networks, can straightforwardly produce labelled directed acyclic graphs and perform semantic dependency parsing.

61, TITLE:      Road Segmentation on low resolution Lidar point clouds for autonomous vehicles
http://arxiv.org/abs/2005.13102
AUTHORS:        Leonardo Gigli ; B Ravi Kiran ; Thomas Paul ; Andres Serna ; Nagarjuna Vemuri ; Beatriz Marcotegui ; Santiago Velasco-Forero
COMMENTS:       ISPRS 2020
HIGHLIGHT:      We introduce the usage of the local normal vector with the LIDAR's spherical coordinates as an input channel to existing LoDNN architectures.

62, TITLE:      Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings
http://arxiv.org/abs/2005.13338
AUTHORS:        Lasse Hansen ; Mattias P. Heinrich
HIGHLIGHT:      Most current works use U-Net-like architectures to predict dense displacement fields from the input images in different supervised and unsupervised settings.

63, TITLE:      Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing
http://arxiv.org/abs/2005.13334
AUTHORS:        Daniel Fernández-González ; Carlos Gómez-Rodríguez
COMMENTS:       Proceedings of ACL 2020. 8 pages
HIGHLIGHT:      In this paper, we show that these results can be improved by using an in-order linearization instead.

64, TITLE:      Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing
http://arxiv.org/abs/2005.13337
AUTHORS:        Liangzhi Li ; Manisha Verma ; Yuta Nakashima ; Ryo Kawasaki ; Hajime Nagahara
COMMENTS:       Accepted in Medical Imaging with Deep Learning (MIDL) 2020
HIGHLIGHT:      To improve the performance of the existing automated methods for retinal image analysis, we propose a two-step vessel classification.

65, TITLE:      Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport
http://arxiv.org/abs/2005.13111
AUTHORS:        Kyle Swanson ; Lili Yu ; Tao Lei
COMMENTS:       To appear at ACL 2020
HIGHLIGHT:      In this work, we extend this selective rationalization approach to text matching, where the goal is to jointly select and align text pieces, such as tokens or sentences, as a justification for the downstream prediction.

66, TITLE:      Evolutionary NAS with Gene Expression Programming of Cellular Encoding
http://arxiv.org/abs/2005.13110
AUTHORS:        Clifford Broni-Bediako ; Yuki Murata ; Luiz Henrique Mormille ; Masayasu Atsumi
HIGHLIGHT:      To address this, we present a new generative encoding scheme -- $symbolic\ linear\ generative\ encoding$ (SLGE) -- simple, yet powerful scheme which embeds local graph transformations in chromosomes of linear fixed-length string to develop CNN architectures of variant shapes and sizes via evolutionary process of gene expression programming.

67, TITLE:      Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints
http://arxiv.org/abs/2005.13109
AUTHORS:        Shushman Choudhury ; Jayesh K. Gupta ; Mykel J. Kochenderfer ; Dorsa Sadigh ; Jeannette Bohg
COMMENTS:       Robotics Science and Systems (RSS) 2020; Source code at https://github.com/sisl/SCoBA.jl
HIGHLIGHT:      We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination and addresses them in a hierarchical manner.

68, TITLE:      GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling
http://arxiv.org/abs/2005.13363
AUTHORS:        Zhuoying Wang ;  YongtaoWang ; Zhi Tang ; Yangyan Li ; Ying Chen ; Haibin Ling ; Weisi Lin
HIGHLIGHT:      In this work, we find that these operations lead to scale-confused features and suboptimal performance because they are spatial-invariant and directly transit all feature information cross scales without spatial selection.

69, TITLE:      Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation
http://arxiv.org/abs/2005.13366
AUTHORS:        Jingyang Zhang ; Guotai Wang ; Hongzhi Xie ; Shuyang Zhang ; Ning Huang ; Shaoting Zhang ; Lixu Gu
HIGHLIGHT:      To alleviate the burden on the annotator, we propose a novel weakly supervised training framework that learns from noisy pseudo labels generated from automatic vessel enhancement, rather than accurate labels obtained by fully manual annotation.

70, TITLE:      A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
http://arxiv.org/abs/2005.13362
AUTHORS:        Edison Marrese-Taylor ; Cristian Rodriguez-Opazo ; Jorge A. Balazs ; Stephen Gould ; Yutaka Matsuo
COMMENTS:       Second Grand Challenge and Workshop on Multimodal Language ACL 2020
HIGHLIGHT:      In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them.

71, TITLE:      Should Answer Immediately or Wait for Further Information? A Novel Wait-or-Answer Task and Its Predictive Approach
http://arxiv.org/abs/2005.13119
AUTHORS:        Zehao Lin ; Shaobo Cui ; Xiaoming Kang ; Guodun Li ; Feng Ji ; Haiqing Chen ; Yin Zhang
COMMENTS:       This previously appeared as arXiv:2002.09616v2, which was mistakenly submitted as a replacement. arXiv admin note: text overlap with arXiv:2002.09616v3
HIGHLIGHT:      Motivated by such interesting quandary, we define a novel task: Wait-or-Answer to better tackle this dilemma faced by dialogue systems.

72, TITLE:      TRIE: End-to-End Text Reading and Information Extraction for Document Understanding
http://arxiv.org/abs/2005.13118
AUTHORS:        Peng Zhang ; Yunlu Xu ; Zhanzhan Cheng ; Shiliang Pu ; Jing Lu ; Liang Qiao ; Yi Niu ; Fei Wu
HIGHLIGHT:      In this paper, we propose a unified end-to-end text reading and information extraction network, where the two tasks can reinforce each other.

73, TITLE:      NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation
http://arxiv.org/abs/2005.13359
AUTHORS:        Cameron Trotter ; Georgia Atkinson ; Matt Sharpe ; Kirsten Richardson ; A. Stephen McGough ; Nick Wright ; Ben Burville ; Per Berggren
COMMENTS:       5 pages, 6 figures, download link, submitted to FGVC7 Workshop @ CVPR20
HIGHLIGHT:      We introduce the Northumberland Dolphin Dataset 2020 (NDD20), a challenging image dataset annotated for both coarse and fine-grained instance segmentation and categorisation.

74, TITLE:      SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition
http://arxiv.org/abs/2005.13117
AUTHORS:        Chengwei Zhang ; Yunlu Xu ; Zhanzhan Cheng ; Shiliang Pu ; Yi Niu ; Fei Wu ; Futai Zou
HIGHLIGHT:      In this work, we introduce a new learnable geometric-unrelated module, the Structure-Preserving Inner Offset Network (SPIN), which allows the color manipulation of source data within the network.

75, TITLE:      Data-Driven Continuum Dynamics via Transport-Teleport Duality
http://arxiv.org/abs/2005.13358
AUTHORS:        Jong-Hoon Ahn
COMMENTS:       28 pages, 8 figures
HIGHLIGHT:      In this study, we propose a mathematical framework for machine learning of transport phenomena.

76, TITLE:      Object-QA: Towards High Reliable Object Quality Assessment
http://arxiv.org/abs/2005.13116
AUTHORS:        Jing Lu ; Baorui Zou ; Zhanzhan Cheng ; Shiliang Pu ; Shuigeng Zhou ; Yi Niu ; Fei Wu
HIGHLIGHT:      In this paper, we define the problem of object quality assessment for the first time and propose an effective approach named Object-QA to assess high-reliable quality scores for object images.

77, TITLE:      Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene
http://arxiv.org/abs/2005.13133
AUTHORS:        Yanliang Zhu ; Dongchun Ren ; Mingyu Fan ; Deheng Qian ; Xin Li ; Huaxia Xia
HIGHLIGHT:      In this paper, we present a novel method for the robust trajectory forecasting of multiple intelligent agents in dynamic scenes.

78, TITLE:      PAI-Conv: Permutable Anisotropic Convolutional Networks for Learning on Point Clouds
http://arxiv.org/abs/2005.13135
AUTHORS:        Zhongpai Gao ; Guangtao Zhai ; Junchi Yan ; Xiaokang Yang
HIGHLIGHT:      In this paper, we propose a permutable anisotropic convolutional operation (PAI-Conv) that calculates soft-permutation matrices for each point according to a set of evenly distributed kernel points on a sphere's surface and performs shared anisotropic filters as CNN does.

79, TITLE:      Efficient Pig Counting in Crowds with Keypoints Tracking and Spatial-aware Temporal Response Filtering
http://arxiv.org/abs/2005.13131
AUTHORS:        Guang Chen ; Shiwen Shen ; Longyin Wen ; Si Luo ; Liefeng Bo
COMMENTS:       IEEE International Conference on Robotics and Automation (ICRA) 2020
HIGHLIGHT:      To that end, we presented a real-time automated pig counting system in crowds using only one monocular fisheye camera with an inspection robot.

80, TITLE:      Rethinking Dialogue State Tracking with Reasoning
http://arxiv.org/abs/2005.13129
AUTHORS:        Lizi Liao ; Yunshan Ma ; Wenqiang Lei ; Tat-Seng Chua
COMMENTS:       accepted by ACL 2020
HIGHLIGHT:      This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data.

81, TITLE:      Counterfactual Detection meets Transfer Learning
http://arxiv.org/abs/2005.13125
AUTHORS:        Kelechi Nwaike ; Licheng Jiao
COMMENTS:       6 pages
HIGHLIGHT:      We can consider Counterfactuals as belonging in the domain of Discourse structure and semantics, A core area in Natural Language Understanding and in this paper, we introduce an approach to resolving counterfactual detection as well as the indexing of the antecedents and consequents of Counterfactual statements.

82, TITLE:      Towards Mesh Saliency Detection in 6 Degrees of Freedom
http://arxiv.org/abs/2005.13127
AUTHORS:        Xiaoying Ding ; Zhenzhong Chen
HIGHLIGHT:      In this work, a novel 6DoF mesh saliency database is developed which provides both the subject's 6DoF data and eye-movement data.

83, TITLE:      SSM-Net for Plants Disease Identification in LowData Regime
http://arxiv.org/abs/2005.13140
AUTHORS:        Shruti Jadon
COMMENTS:       5 pages, 6 Figures
HIGHLIGHT:      In this paper, we propose a new metrics-based few-shot learning SSM net architecture which consists of stacked siamese and matching network components to solve the problem of disease detection in low data regimes.

84, TITLE:      Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking
http://arxiv.org/abs/2005.13139
AUTHORS:        Geoffrey Clark ; Joseph Campbell ; Seyed Mostafa Rezayat Sorkhabadi ; Wenlong Zhang ; Heni Ben Amor
COMMENTS:       Accepted to ICRA 2020. Accompanying video presentation: https://www.youtube.com/watch?v=EjSVjueePyQ&t=1s
HIGHLIGHT:      We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior.

85, TITLE:      The First Shared Task on Discourse Representation Structure Parsing
http://arxiv.org/abs/2005.13399
AUTHORS:        Lasha Abzianidze ; Rik van Noord ; Hessel Haagsma ; Johan Bos
COMMENTS:       International Conference on Computational Semantics (IWCS)
HIGHLIGHT:      The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to produce Discourse Representation Structures (DRSs) for English sentences.

86, TITLE:      MT-Adapted Datasheets for Datasets: Template and Repository
http://arxiv.org/abs/2005.13156
AUTHORS:        Marta R. Costa-jussà ; Roger Creus ; Oriol Domingo ; Albert Domínguez ; Miquel Escobar ; Cayetana López ; Marina Garcia ; Margarita Geleta
HIGHLIGHT:      In this report we are taking the standardized model proposed by Gebru et al. (2018) for documenting the popular machine translation datasets of the EuroParl (Koehn, 2005) and News-Commentary (Barrault et al., 2019).

87, TITLE:      False Positive Removal for 3D Vehicle Detection with Penetrated Point Classifier
http://arxiv.org/abs/2005.13153
AUTHORS:        Sungmin Woo ; Sangwon Hwang ; Woojin Kim ; Junhyeop Lee ; Dogyoon Lee ; Sangyoun Lee
HIGHLIGHT:      To address the issue, we introduce Penetrated Point Classifier (PPC) based on the underlying property of LiDAR that points cannot be generated behind vehicles.

88, TITLE:      On Mutual Information in Contrastive Learning for Visual Representations
http://arxiv.org/abs/2005.13149
AUTHORS:        Mike Wu ; Chengxu Zhuang ; Milan Mosse ; Daniel Yamins ; Noah Goodman
COMMENTS:       11 pages content; 9 pages supplement with proofs
HIGHLIGHT:      In practice, our new objectives yield representations that outperform those learned with previous approaches for transfer to classification, bounding box detection, instance segmentation, and keypoint detection.



==========Updates to Previous Papers==========
1, TITLE:       Robust Image Segmentation Quality Assessment
http://arxiv.org/abs/1903.08773
AUTHORS:        Leixin Zhou ; Wenxiang Deng ; Xiaodong Wu
HIGHLIGHT:      In this paper, we propose to alleviate this problem by utilizing the difference between the input image and the reconstructed image, which is conditioned on the segmentation to be assessed, to lower the chance to overfit to the undesired image features from the original input image, and thus to increase the robustness.

2, TITLE:       Temporal Knowledge Graph completion based on time series Gaussian embedding
http://arxiv.org/abs/1911.07893
AUTHORS:        Chengjin Xu ; Mojtaba Nayyeri ; Fouad Alkhoury ; Jens Lehmann ; Hamed Shariat Yazdi
HIGHLIGHT:      In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition.

3, TITLE:       A Simple Baseline for Multi-Object Tracking
http://arxiv.org/abs/2004.01888
AUTHORS:        Yifu Zhang ; Chunyu Wang ; Xinggang Wang ; Wenjun Zeng ; Wenyu Liu
HIGHLIGHT:      In this work, we study the essential reasons behind the failure, and accordingly present a simple baseline to addresses the problem.

4, TITLE:       Learning to Recognize Discontiguous Entities
http://arxiv.org/abs/1810.08579
AUTHORS:        Aldrian Obaja Muis ; Wei Lu
COMMENTS:       9+1 pages + 8 pages supplementary, published in EMNLP 2016. v2: fix references. v3: include missing supplementary, update with code repository
HIGHLIGHT:      Motivated by a previous work, we propose to use a novel hypergraph representation to jointly encode discontiguous entities of unbounded length, which can overlap with one another.

5, TITLE:       Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
http://arxiv.org/abs/1912.01752
AUTHORS:        Benjamin A. Toms ; Elizabeth A. Barnes ; Imme Ebert-Uphoff
COMMENTS:       The second version of this manuscript is currently under review at the Journal of Advances in Modeling Earth Systems (JAMES)
HIGHLIGHT:      Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself.

6, TITLE:       Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey
http://arxiv.org/abs/2002.06612
AUTHORS:        Zahra Abbasiantaeb ; Saeedeh Momtazi
HIGHLIGHT:      In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both traditional information retrieval perspective, and more recent deep neural network perspective. We also introduce well-known datasets for the task and present available results from the literature to have a comparison between different techniques.

7, TITLE:       Elucidating image-to-set prediction: An analysis of models, losses and datasets
http://arxiv.org/abs/1904.05709
AUTHORS:        Luis Pineda ; Amaia Salvador ; Michal Drozdzal ; Adriana Romero
HIGHLIGHT:      In this paper, we identify an important reproducibility challenge in the image-to-set prediction literature that impedes proper comparisons among published methods, namely, researchers use different evaluation protocols to assess their contributions.

8, TITLE:       Neural Reverse Engineering of Stripped Binaries
http://arxiv.org/abs/1902.09122
AUTHORS:        Yaniv David ; Uri Alon ; Eran Yahav
HIGHLIGHT:      We present a novel approach for predicting procedure names in stripped executables.

9, TITLE:       TightCap: 3D Human Shape Capture with Clothing Tightness
http://arxiv.org/abs/1904.02601
AUTHORS:        Xin Chen ; Anqi Pang ; Yang Wei ; Lan Xui ; Jingyi Yu
COMMENTS:       14 pages, 17 figures
HIGHLIGHT:      In this paper, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single 3D human scan, which enables numerous applications such as virtual try-on, biometrics and body evaluation. We further propose a new clothing tightness dataset (CTD) of human scans with a large variety of clothing styles, poses and corresponding ground-truth human shapes to stimulate further research.

10, TITLE:      Table Search Using a Deep Contextualized Language Model
http://arxiv.org/abs/2005.09207
AUTHORS:        Zhiyu Chen ; Mohamed Trabelsi ; Jeff Heflin ; Yinan Xu ; Brian D. Davison
COMMENTS:       Accepted at SIGIR 2020 (Long)
HIGHLIGHT:      In this paper, we use the deep contextualized language model BERT for the task of ad hoc table retrieval.

11, TITLE:      Visual Interest Prediction with Attentive Multi-Task Transfer Learning
http://arxiv.org/abs/2005.12770
AUTHORS:        Deepanway Ghosal ; Maheshkumar H. Kolekar
HIGHLIGHT:      In this paper, we propose a transfer learning and attention mechanism based neural network model to predict visual interest & affective dimensions in digital photos.

12, TITLE:      GPT-too: A language-model-first approach for AMR-to-text generation
http://arxiv.org/abs/2005.09123
AUTHORS:        Manuel Mager ; Ramon Fernandez Astudillo ; Tahira Naseem ; Md Arafat Sultan ; Young-Suk Lee ; Radu Florian ; Salim Roukos
COMMENTS:       Paper accepted to the Annual Meeting of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring.

13, TITLE:      Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
http://arxiv.org/abs/1906.04571
AUTHORS:        Ran Zmigrod ; Sabrina J. Mielke ; Hanna Wallach ; Ryan Cotterell
COMMENTS:       ACL 2019
HIGHLIGHT:      We present a novel approach for converting between masculine-inflected and feminine-inflected sentences in such languages.

14, TITLE:      Y-net: Biomedical Image Segmentation and Clustering
http://arxiv.org/abs/2004.05698
AUTHORS:        Sharmin Pathan ; Anant Tripathi
HIGHLIGHT:      We propose a deep clustering architecture alongside image segmentation for medical image analysis.

15, TITLE:      Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types
http://arxiv.org/abs/2005.12762
AUTHORS:        Belen Saldias ; Deb Roy
COMMENTS:       9 pages, Proceedings of the 2020 ACL Workshop on Narrative Understanding, Storylines, and Events (NUSE). ACL
HIGHLIGHT:      We show that actions followed by the narrator's evaluation of these are the aspects non-experts consider the most. To address this challenge, we first introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts.

16, TITLE:      Score-Guided Generative Adversarial Networks
http://arxiv.org/abs/2004.04396
AUTHORS:        Minhyeok Lee ; Junhee Seok
HIGHLIGHT:      We propose a Generative Adversarial Network (GAN) that introduces an evaluator module using pre-trained networks.

17, TITLE:      City-GAN: Learning architectural styles using a custom Conditional GAN architecture
http://arxiv.org/abs/1907.05280
AUTHORS:        Maximilian Bachl ; Daniel C. Ferreira
HIGHLIGHT:      We use GANs to learn architectural features of major cities and to generate images of buildings which do not exist.

18, TITLE:      Unifying Training and Inference for Panoptic Segmentation
http://arxiv.org/abs/2001.04982
AUTHORS:        Qizhu Li ; Xiaojuan Qi ; Philip H. S. Torr
COMMENTS:       CVPR 2020
HIGHLIGHT:      We present an end-to-end network to bridge the gap between training and inference pipeline for panoptic segmentation, a task that seeks to partition an image into semantic regions for "stuff" and object instances for "things".

19, TITLE:      On the Cross-lingual Transferability of Monolingual Representations
http://arxiv.org/abs/1910.11856
AUTHORS:        Mikel Artetxe ; Sebastian Ruder ; Dani Yogatama
COMMENTS:       ACL 2020
HIGHLIGHT:      State-of-the-art unsupervised multilingual models (e.g., multilingual BERT) have been shown to generalize in a zero-shot cross-lingual setting.

20, TITLE:      Self-Supervised Representation Learning on Document Images
http://arxiv.org/abs/2004.10605
AUTHORS:        Adrian Cosma ; Mihai Ghidoveanu ; Michael Panaitescu-Liess ; Marius Popescu
COMMENTS:       15 pages, 5 figures. Accepted at DAS 2020: IAPR International Workshop on Document Analysis Systems
HIGHLIGHT:      We propose two context-aware alternatives to improve performance on the Tobacco-3482 image classification task.

21, TITLE:      AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
http://arxiv.org/abs/1905.13209
AUTHORS:        Michael S. Ryoo ; AJ Piergiovanni ; Mingxing Tan ; Anelia Angelova
HIGHLIGHT:      We interpret a video CNN as a collection of multi-stream convolutional blocks connected to each other, and propose the approach of automatically finding neural architectures with better connectivity and spatio-temporal interactions for video understanding.

22, TITLE:      Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking
http://arxiv.org/abs/2004.02108
AUTHORS:        Shi Yin ; Shangfei Wang ; Xiaoping Chen ; Enhong Chen
HIGHLIGHT:      To address this, we propose a novel attentive one-dimensional heatmap regression method for facial landmark localization.

23, TITLE:      Goal-conditioned Imitation Learning
http://arxiv.org/abs/1906.05838
AUTHORS:        Yiming Ding ; Carlos Florensa ; Mariano Phielipp ; Pieter Abbeel
COMMENTS:       Published at NeurIPS 2019
HIGHLIGHT:      In this work we investigate different approaches to incorporate demonstrations to drastically speed up the convergence to a policy able to reach any goal, also surpassing the performance of an agent trained with other Imitation Learning algorithms.

24, TITLE:      Quantification of Tomographic Patterns associated with COVID-19 from Chest CT
http://arxiv.org/abs/2004.01279
AUTHORS:        Shikha Chaganti ; Abishek Balachandran ; Guillaume Chabin ; Stuart Cohen ; Thomas Flohr ; Bogdan Georgescu ; Philippe Grenier ; Sasa Grbic ; Siqi Liu ; François Mellot ; Nicolas Murray ; Savvas Nicolaou ; William Parker ; Thomas Re ; Pina Sanelli ; Alexander W. Sauter ; Zhoubing Xu ; Youngjin Yoo ; Valentin Ziebandt ; Dorin Comaniciu
HIGHLIGHT:      Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

25, TITLE:      Target-Tailored Source-Transformation for Scene Graph Generation
http://arxiv.org/abs/1904.02104
AUTHORS:        Wentong Liao ; Cuiling Lan ; Wenjun Zeng ; Michael Ying Yang ; Bodo Rosenhahn
HIGHLIGHT:      In this work, we argue that a source object should give what tar-get object needs and give different objects different information rather than contributing common information to all targets.

26, TITLE:      Can Neural Networks Learn Symbolic Rewriting?
http://arxiv.org/abs/1911.04873
AUTHORS:        Bartosz Piotrowski ; Josef Urban ; Chad E. Brown ; Cezary Kaliszyk
HIGHLIGHT:      This work investigates if the current neural architectures are adequate for learning symbolic rewriting.

27, TITLE:      Adversarial Examples for Models of Code
http://arxiv.org/abs/1910.07517
AUTHORS:        Noam Yefet ; Uri Alon ; Eran Yahav
HIGHLIGHT:      In this paper, we show that these models are vulnerable to adversarial examples, and introduce a novel approach for attacking trained models of code with adversarial examples.

28, TITLE:      Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution
http://arxiv.org/abs/1501.00960
AUTHORS:        Eitan Adam Pechenick ; Christopher M. Danforth ; Peter Sheridan Dodds
COMMENTS:       13 pages, 16 figures
HIGHLIGHT:      We highlight these dynamics by examining and comparing major contributions to the statistical divergence of English data sets between decades in the period 1800--2000.

29, TITLE:      Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet
http://arxiv.org/abs/1907.03220
AUTHORS:        Saket S. Chaturvedi ; Kajol Gupta ; Prakash. S. Prasad
COMMENTS:       This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_15
HIGHLIGHT:      In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists.

30, TITLE:      Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data
http://arxiv.org/abs/2005.05414
AUTHORS:        Soumya Banerjee ; Debarshi Kumar Sanyal ; Samiran Chattopadhyay ; Plaban Kumar Bhowmick ; Parthapratim Das
COMMENTS:       to appear in the proceedings of JCDL'2020
HIGHLIGHT:      In this paper, we address this problem using transfer learning.

31, TITLE:      Real-Time Semantic Background Subtraction
http://arxiv.org/abs/2002.04993
AUTHORS:        Anthony Cioppa ; Marc Van Droogenbroeck ; Marc Braham
COMMENTS:       Accepted and Published at ICIP 2020
HIGHLIGHT:      In this paper, we present a novel background subtraction algorithm called Real-Time Semantic Background Subtraction (denoted RT-SBS) which extends SBS for real-time constrained applications while keeping similar performances.

32, TITLE:      Unsupervised Learning for Intrinsic Image Decomposition from a Single Image
http://arxiv.org/abs/1911.09930
AUTHORS:        Yunfei Liu ; Yu Li ; Shaodi You ; Feng Lu
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      In this paper, we propose a novel unsupervised intrinsic image decomposition framework, which relies on neither labeled training data nor hand-crafted priors.

33, TITLE:      MOPT: Multi-Object Panoptic Tracking
http://arxiv.org/abs/2004.08189
AUTHORS:        Juana Valeria Hurtado ; Rohit Mohan ; Wolfram Burgard ; Abhinav Valada
COMMENTS:       Code & models are available at http://rl.uni-freiburg.de/research/panoptictracking
HIGHLIGHT:      In this paper, we introduce a novel perception task denoted as multi-object panoptic tracking (MOPT), which unifies the conventionally disjoint tasks of semantic segmentation, instance segmentation, and multi-object tracking.

34, TITLE:      C3VQG: Category Consistent Cyclic Visual Question Generation
http://arxiv.org/abs/2005.07771
AUTHORS:        Shagun Uppal ; Anish Madan ; Sarthak Bhagat ; Yi Yu ; Rajiv Ratn Shah
HIGHLIGHT:      In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers.

35, TITLE:      Monitoring and Diagnosability of Perception Systems
http://arxiv.org/abs/2005.11816
AUTHORS:        Pasquale Antonante ; David I. Spivak ; Luca Carlone
HIGHLIGHT:      In this work, we propose a mathematical model for runtime monitoring and fault detection of perception systems.

36, TITLE:      Learning Semantic Program Embeddings with Graph Interval Neural Network
http://arxiv.org/abs/2005.09997
AUTHORS:        Yu Wang ; Fengjuan Gao ; Linzhang Wang ; Ke Wang
COMMENTS:       The abstract is simplified, for full abstract, please refer to the paper
HIGHLIGHT:      In this paper, we present a new graph neural architecture, called Graph Interval Neural Network (GINN), to tackle the weaknesses of the existing GNN.

37, TITLE:      Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning
http://arxiv.org/abs/2004.10888
AUTHORS:        Shangtong Zhang ; Bo Liu ; Shimon Whiteson
HIGHLIGHT:      We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a discounted infinite horizon MDP.

38, TITLE:      Tag-less Back-Translation
http://arxiv.org/abs/1912.10514
AUTHORS:        Idris Abdulmumin ; Bashir Shehu Galadanci ; Aliyu Garba
COMMENTS:       Submitted to Machine Translation, Springer; 22 pages, 4 figures, 11 tables
HIGHLIGHT:      This work presents pre-training and fine-tuning as a simplified but more effective approach of differentiating between the two data.

39, TITLE:      Complexity phase diagram for interacting and long-range bosonic Hamiltonians
http://arxiv.org/abs/1906.04178
AUTHORS:        Nishad Maskara ; Abhinav Deshpande ; Adam Ehrenberg ; Minh C. Tran ; Bill Fefferman ; Alexey V. Gorshkov
COMMENTS:       15 pages, 5 figures. v2: 19 pages, 7 figures
HIGHLIGHT:      We classify phases of a bosonic lattice model based on the computational complexity of classically simulating the system.

40, TITLE:      Articulation-aware Canonical Surface Mapping
http://arxiv.org/abs/2004.00614
AUTHORS:        Nilesh Kulkarni ; Abhinav Gupta ; David F. Fouhey ; Shubham Tulsiani
COMMENTS:       To appear at CVPR 2020, project page https://nileshkulkarni.github.io/acsm/
HIGHLIGHT:      Our key insight is that these tasks are geometrically related, and we can obtain supervisory signal via enforcing consistency among the predictions.

41, TITLE:      Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
http://arxiv.org/abs/1909.08473
AUTHORS:        Lei Kang ; Marçal Rusiñol ; Alicia Fornés ; Pau Riba ; Mauricio Villegas
COMMENTS:       Accepted to WACV 2020
HIGHLIGHT:      In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer.

42, TITLE:      Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning
http://arxiv.org/abs/2003.13503
AUTHORS:        Aditya Khamparia ; Subrato Bharati ; Prajoy Podder ; Deepak Gupta ; Ashish Khanna ; Thai Kim Phung ; Dang N. H. Thanh
COMMENTS:       24 pages, 11 figures
HIGHLIGHT:      Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper.

43, TITLE:      KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
http://arxiv.org/abs/1910.06180
AUTHORS:        Vlad Hosu ; Hanhe Lin ; Tamas Sziranyi ; Dietmar Saupe
COMMENTS:       Published
HIGHLIGHT:      We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images.

44, TITLE:      miniKanren as a Tool for Symbolic Computation in Python
http://arxiv.org/abs/2005.11644
AUTHORS:        Brandon T. Willard
HIGHLIGHT:      In this article, we give a brief overview of the current state and future potential of symbolic computation within the Python statistical modeling and machine learning community.

45, TITLE:      Semantic Segmentation for Compound figures
http://arxiv.org/abs/1912.07142
AUTHORS:        Weixin Jiang ; Eric Schwenker ; Maria Chan ; Oliver Cossairt
COMMENTS:       major modification
HIGHLIGHT:      In this paper, we propose a semantic segmentation approach for compound figure separation, decomposing the compound figures into "master images".

46, TITLE:      Obfuscation for Privacy-preserving Syntactic Parsing
http://arxiv.org/abs/1904.09585
AUTHORS:        Zhifeng Hu ; Serhii Havrylov ; Ivan Titov ; Shay B. Cohen
COMMENTS:       Accepted to IWPT 2020
HIGHLIGHT:      We introduce an idea for a privacy-preserving transformation on natural language data, inspired by homomorphic encryption.

47, TITLE:      Algebraic Global Gadgetry for Surjective Constraint Satisfaction
http://arxiv.org/abs/2005.11307
AUTHORS:        Hubie Chen
HIGHLIGHT:      We present an algebraic framework for proving hardness results on surjective CSPs; essentially, this framework computes global gadgetry that permits one to present a reduction from a classical CSP to a surjective CSP.

48, TITLE:      End-to-End Object Detection with Transformers
http://arxiv.org/abs/2005.12872
AUTHORS:        Nicolas Carion ; Francisco Massa ; Gabriel Synnaeve ; Nicolas Usunier ; Alexander Kirillov ; Sergey Zagoruyko
HIGHLIGHT:      We present a new method that views object detection as a direct set prediction problem.

49, TITLE:      Long-Term Cloth-Changing Person Re-identification
http://arxiv.org/abs/2005.12633
AUTHORS:        Xuelin Qian ; Wenxuan Wang ; Li Zhang ; Fangrui Zhu ; Yanwei Fu ; Tao Xiang ; Yu-Gang Jiang ; Xiangyang Xue
COMMENTS:       24 pages, 10 figures, 5 tables
HIGHLIGHT:      In this work, we focus on a much more difficult yet practical setting where person matching is conducted over long-duration, e.g., over days and months and therefore inevitably under the new challenge of changing clothes.

50, TITLE:      Patch-based Non-Local Bayesian Networks for Blind Confocal Microscopy Denoising
http://arxiv.org/abs/2003.11177
AUTHORS:        Saeed Izadi ; Ghassan Hamarneh
HIGHLIGHT:      In this work, we propose to exploit the strengths of NLB in the framework of Bayesian deep learning.

51, TITLE:      Towards Understanding Gender Bias in Relation Extraction
http://arxiv.org/abs/1911.03642
AUTHORS:        Andrew Gaut ; Tony Sun ; Shirlyn Tang ; Yuxin Huang ; Jing Qian ; Mai ElSherief ; Jieyu Zhao ; Diba Mirza ; Elizabeth Belding ; Kai-Wei Chang ; William Yang Wang
HIGHLIGHT:      We create WikiGenderBias, a distantly supervised dataset with a human annotated test set.

52, TITLE:      Intersectional Bias in Hate Speech and Abusive Language Datasets
http://arxiv.org/abs/2005.05921
AUTHORS:        Jae Yeon Kim ; Carlos Ortiz ; Sarah Nam ; Sarah Santiago ; Vivek Datta
COMMENTS:       Incorporated comments from the ICWSM reviewers
HIGHLIGHT:      Algorithms are widely applied to detect hate speech and abusive language in social media.

53, TITLE:      DeepInSAR: A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation
http://arxiv.org/abs/1909.03120
AUTHORS:        Xinyao Sun ; Aaron Zimmer ; Subhayan Mukherjee ; Navaneeth Kamballur Kottayil ; Parwant Ghuman ; Irene Cheng
COMMENTS:       19 pages
HIGHLIGHT:      In this work, we propose a deep convolutional neural network (CNN) based model DeepInSAR to intelligently solve both the phase filtering and coherence estimation problems.

54, TITLE:      Analyzing Smart Contracts: From EVM to a sound Control-Flow Graph
http://arxiv.org/abs/2004.14437
AUTHORS:        Elvira Albert ; Jesús Correas ; Pablo Gordillo ; Alejandro Hernández-Cerezo Guillermo Román-Díez ; Albert Rubio
HIGHLIGHT:      This report addresses the problem of obtaining a precise and complete stack-sensitive CFG by means of a static analysis, cloning the blocks that might be executed using different states of the execution stack.

55, TITLE:      Proving P!=NP in first-order PA
http://arxiv.org/abs/2005.10080
AUTHORS:        Rupert McCallum
HIGHLIGHT:      We show that it is provable in PA that there is an arithmetically definable sequence $\{\phi_{n}:n \in \omega\}$ of $\Pi^{0}_{2}$-sentences, such that - PRA+$\{\phi_{n}:n \in \omega\}$ is $\Pi^{0}_{2}$-sound and $\Pi^{0}_{1}$-complete - the length of $\phi_{n}$ is bounded above by a polynomial function of $n$ with positive leading coefficient - PRA+$\phi_{n+1}$ always proves 1-consistency of PRA+$\phi_{n}$.

56, TITLE:      ProbaNet: Proposal-balanced Network for Object Detection
http://arxiv.org/abs/2005.02699
AUTHORS:        Jing Wu ; Xiang Zhang ; Mingyi Zhou ; Ce Zhu
HIGHLIGHT:      In this study, we propose a Proposal-balanced Network (ProbaNet) for alleviating the imbalance problem.

57, TITLE:      The Lagrangian remainder of Taylor's series, distinguishes $\mathcal{O}(f(x))$ time complexities to polynomials or not
http://arxiv.org/abs/2001.11811
AUTHORS:        Nikolaos P. Bakas ; Elias Kosmatopoulos ; Mihalis Nicolaou ; Savvas A. Chatzichristofis
HIGHLIGHT:      The purpose of this letter is to investigate the time complexity consequences of the truncated Taylor series, known as Taylor Polynomials \cite{bakas2019taylor,Katsoprinakis2011,Nestoridis2011}.

58, TITLE:      Simplify-then-Translate: Automatic Preprocessing for Black-Box Machine Translation
http://arxiv.org/abs/2005.11197
AUTHORS:        Sneha Mehta ; Bahareh Azarnoush ; Boris Chen ; Avneesh Saluja ; Vinith Misra ; Ballav Bihani ; Ritwik Kumar
HIGHLIGHT:      In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification.

59, TITLE:      The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
http://arxiv.org/abs/2004.14003
AUTHORS:        Arjun D. Desai ; Francesco Caliva ; Claudia Iriondo ; Naji Khosravan ; Aliasghar Mortazi ; Sachin Jambawalikar ; Drew Torigian ; Jutta Ellermann ; Mehmet Akcakaya ; Ulas Bagci ; Radhika Tibrewala ; Io Flament ; Matthew O`Brien ; Sharmila Majumdar ; Mathias Perslev ; Akshay Pai ; Christian Igel ; Erik B. Dam ; Sibaji Gaj ; Mingrui Yang ; Kunio Nakamura ; Xiaojuan Li ; Cem M. Deniz ; Vladimir Juras ; Ravinder Regatte ; Garry E. Gold ; Brian A. Hargreaves ; Valentina Pedoia ; Akshay S. Chaudhari
COMMENTS:       Submitted to Radiology: Artificial Intelligence; Fixed typos
HIGHLIGHT:      The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

60, TITLE:      CineFilter: Unsupervised Filtering for Real Time Autonomous Camera Systems
http://arxiv.org/abs/1912.05636
AUTHORS:        Sudheer Achary ; K L Bhanu Moorthy ; Syed Ashar Javed ; Nikita Shravan ; Vineet Gandhi ; Anoop Namboodiri
HIGHLIGHT:      In this paper, we propose two online filtering methods called Cinefilters, which produce smooth camera trajectories that are motivated by cinematographic principles.

61, TITLE:      g2pM: A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset
http://arxiv.org/abs/2004.03136
AUTHORS:        Kyubyong Park ; Seanie Lee
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      Motivated by these, in this work, we introduce a new benchmark dataset that consists of 99,000+ sentences for Chinese polyphone disambiguation.

62, TITLE:      Video Modeling with Correlation Networks
http://arxiv.org/abs/1906.03349
AUTHORS:        Heng Wang ; Du Tran ; Lorenzo Torresani ; Matt Feiszli
HIGHLIGHT:      This paper proposes an alternative approach based on a learnable correlation operator that can be used to establish frame-toframe matches over convolutional feature maps in the different layers of the network.