2020.04.16.txt

==========New Papers==========

1, TITLE:       A Transductive Approach for Video Object Segmentation

http://arxiv.org/abs/2004.07193

AUTHORS:        Zhang Yizhuo ; Wu Zhirong ; Peng Houwen ; Lin Stephen

COMMENTS:       To Appear in CVPR 2020

HIGHLIGHT:      To address this issue, we propose a simple yet strong 
transductive method, in which additional modules, datasets, and 
dedicated architectural designs are not needed.



2, TITLE:       Continuous learning of face attribute synthesis

http://arxiv.org/abs/2004.06904

AUTHORS:        Xin Ning ; Shaohui Xu ; Xiaoli Dong ; Weijun Li ; Fangzhe Nan ; Yuanzhou Yao

HIGHLIGHT:      To overcome the limitations of a single network in new 
attribute synthesis, a continuous learning method for face attribute 
synthesis is proposed in this work.



3, TITLE:       Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB Images

http://arxiv.org/abs/2004.06930

AUTHORS:        D. Sabari Nathan ; K. Uma ; D Synthiya Vinothini ; B. Sathya Bama ; S. M. Md Mansoor Roomi

COMMENTS:       6pages,4 figures

HIGHLIGHT:      This work proposes a novel light weight network with 
very less number of parameters about 233,059 parameters based on 
Residual dense model with attention mechanism to obtain this solution.



4, TITLE:       Combining Visible Light and Infrared Imaging for 
Efficient Detection of Respiratory Infections such as COVID-19 on 
Portable Device

http://arxiv.org/abs/2004.06912

AUTHORS:        Zheng Jiang ; Menghan Hu ; Lei Fan ; Yaling Pan ; Wei Tang ; Guangtao Zhai ; Yong Lu

HIGHLIGHT:      Therefore, in this paper, we propose a portable 
non-contact method to screen the health condition of people wearing 
masks through analysis of the respiratory characteristics.



5, TITLE:       Explaining Regression Based Neural Network Model

http://arxiv.org/abs/2004.06918

AUTHORS:        Mégane Millan ; Catherine Achard

COMMENTS:       7 pages, 5 figures, 4 tables

HIGHLIGHT:      In this work, we design an experimental settings where 
the ground truth can been established: we generate ideal signals and 
disrupted signals with errors and learn a neural network that determines
 the quality of the signals.



6, TITLE:       A Novel CNN-based Method for Accurate Ship Detection in 
HR Optical Remote Sensing Images via Rotated Bounding Box

http://arxiv.org/abs/2004.07124

AUTHORS:        Linhao Li ; Zhiqiang Zhou ; Bo Wang ; Lingjuan Miao ; Hua Zong

HIGHLIGHT:      In this paper, a novel CNN-based ship detection method 
is proposed, by overcoming some common deficiencies of current CNN-based
 methods in ship detection.



7, TITLE:       Fully Convolutional Online Tracking

http://arxiv.org/abs/2004.07109

AUTHORS:        Yutao Cui ; Cheng Jiang ; Limin Wang ; Gangshan Wu

COMMENTS:       Technical Report. Code will be made available at https://github.com/MCG-NJU/FCOT

HIGHLIGHT:      In this paper, we present the first fully convolutional 
online tracking framework (FCOT), with a focus on enabling online 
learning for both classification and regression branches.



8, TITLE:       From Understanding Genetic Drift to a Smart-Restart Parameter-less Compact Genetic Algorithm

http://arxiv.org/abs/2004.07141

AUTHORS:        Benjamin Doerr ; Weijie Zheng

COMMENTS:       4 figures

HIGHLIGHT:      Based on a recent quantitative analysis which population
 sizes lead to genetic drift, we propose a parameter-less version of the
 compact genetic algorithm that automatically finds a suitable 
population size without spending too much time in situations unfavorable
 due to genetic drift.



9, TITLE:       Transfer-Learning-Aware Neuro-Evolution for Diseases Detection in Chest X-Ray Images

http://arxiv.org/abs/2004.07136

AUTHORS:        Albert Susanto ;  Herman ; Tjeng Wawan Cenggoro ;  Suharjito ; Bens Pardamean

HIGHLIGHT:      To overcome this problem, neuro-evolution using a 
genetic algorithm can be used to find the best architecture for transfer
 learning.



10, TITLE:      Residual-driven Fuzzy C-Means Clustering for Image Segmentation

http://arxiv.org/abs/2004.07160

AUTHORS:        Cong Wang ; Witold Pedrycz ; ZhiWu Li ; MengChu Zhou

COMMENTS:       14 pages, 13 figures, 6 tables

HIGHLIGHT:      We propose a residual-driven FCM framework by 
integrating into FCM a residual-related fidelity term derived from the 
distribution of different types of noise.



11, TITLE:      Document-level Representation Learning using Citation-informed Transformers

http://arxiv.org/abs/2004.07180

AUTHORS:        Arman Cohan ; Sergey Feldman ; Iz Beltagy ; Doug Downey ; Daniel S. Weld

COMMENTS:       ACL 2020

HIGHLIGHT:      We propose SPECTER, a new method to generate 
document-level embedding of scientific documents based on pretraining a 
Transformer language model on a powerful signal of document-level 
relatedness: the citation graph.



12, TITLE:      Bias in Multimodal AI: Testbed for Fair Automatic Recruitment

http://arxiv.org/abs/2004.07173

AUTHORS:        Alejandro Peña ; Ignacio Serna ; Aythami Morales ; Julian Fierrez

HIGHLIGHT:      With the aim of studying how current multimodal 
algorithms based on heterogeneous sources of information are affected by
 sensitive elements and inner biases in the data, we propose a 
fictitious automated recruitment testbed: FairCVtest.



13, TITLE:      Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model

http://arxiv.org/abs/2004.06793

AUTHORS:        Toktam A. Oghaz ; Ece C. Mutlu ; Jasser Jasser ; Niloofar Yousefi ; Ivan Garibay

COMMENTS:       9 pages, 4 figures

HIGHLIGHT:      To address this issue, we propose a novel event-based narrative summary extraction framework.



14, TITLE:      RoboTHOR: An Open Simulation-to-Real Embodied AI Platform

http://arxiv.org/abs/2004.06799

AUTHORS:        Matt Deitke ; Winson Han ; Alvaro Herrasti ; Aniruddha 
Kembhavi ; Eric Kolve ; Roozbeh Mottaghi ; Jordi Salvador ; Dustin 
Schwenk ; Eli VanderBilt ; Matthew Wallingford ; Luca Weihs ; Mark 
Yatskar ; Ali Farhadi

COMMENTS:       CVPR 2020

HIGHLIGHT:      In this paper, we introduce RoboTHOR to democratize research in interactive and embodied visual AI.



15, TITLE:      Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans

http://arxiv.org/abs/2004.06780

AUTHORS:        Taimur Hassan ; Samet Akcay ; Mohammed Bennamoun ; Salman Khan ; Naoufel Werghi

HIGHLIGHT:      This paper presents a cascaded structure tensor 
framework that can automatically extract and recognize suspicious items 
in heavily occluded and cluttered baggage.



16, TITLE:      Entities as Experts: Sparse Memory Access with Entity Supervision

http://arxiv.org/abs/2004.07202

AUTHORS:        Thibault Févry ; Livio Baldini Soares ; Nicholas FitzGerald ; Eunsol Choi ; Tom Kwiatkowski

HIGHLIGHT:      We introduce a new model, Entities as Experts (EaE), 
that can access distinct memories of the entities mentioned in a piece 
of text.



17, TITLE:      BabyAI++: Towards Grounded-Language Learning beyond Memorization

http://arxiv.org/abs/2004.07200

AUTHORS:        Tianshi Cao ; Jingkang Wang ; Yining Zhang ; Sivabalan Manivasagam

COMMENTS:       Accepted to the ICLR 2020 workshop: Beyond tabula rasa in RL (BeTR-RL)

HIGHLIGHT:      To promote research in this direction, we introduce a 
new platform, BabyAI++, to generate various dynamic environments along 
with corresponding descriptive texts.



18, TITLE:      Using Player's Body-Orientation to Model Pass Feasibility in Soccer

http://arxiv.org/abs/2004.07209

AUTHORS:        Adrià Arbués-Sangüesa ; Adrián Martín ; Javier Fernández ; Coloma Ballester ; Gloria Haro

COMMENTS:       Accepted at the Computer Vision in Sports Workshop at CVPR 2020

HIGHLIGHT:      Given a monocular video of a soccer match, this paper 
presents a computational model to estimate the most feasible pass at any
 given time.



19, TITLE:      On Box-Cox Transformation for Image Normality and Pattern Classification

http://arxiv.org/abs/2004.07210

AUTHORS:        Abbas Cheddad

COMMENTS:       The paper has an Appendix attached to it

HIGHLIGHT:      We compare the effect of this light-weight Box-Cox 
transformation with well-established state-of-the-art low light image 
enhancement techniques.



20, TITLE:      Improving Many-objective Evolutionary Algorithms by Means of Expanded Cone Orders

http://arxiv.org/abs/2004.06941

AUTHORS:        Yali Wang ; André Deutz ; Thomas Bäck ; Michael T. M. Emmerich

HIGHLIGHT:      As a remedy to stagnation of search in many objective 
optimization, in this paper, we suggest to enhance the Pareto dominance 
order by involving a convex obtuse dominance cone in the convergence 
phase of an evolutionary optimization algorithm.



21, TITLE:      Efficient, Near Complete and Often Sound Hybrid Dynamic Data Race Prediction (extended version)

http://arxiv.org/abs/2004.06969

AUTHORS:        Martin Sulzmann ; Kai Stadtmüller

HIGHLIGHT:      We introduce an efficient, near complete and often sound
 dynamic data race prediction method that combines the lockset method 
with several improvements made in the area of happens-before methods. We
 provide extensive experimental data that shows that our method works 
well in practice.



22, TITLE:      Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms

http://arxiv.org/abs/2004.06967

AUTHORS:        Matteo Luperto ; Luca Fochetta ; Francesco Amigoni

HIGHLIGHT:      In this paper, we present an approach that exploits a 
prediction of the geometric structure of the unknown parts of an 
environment to improve exploration performance.



23, TITLE:      ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos

http://arxiv.org/abs/2004.06971

AUTHORS:        Guillaume Vaudaux-Ruth ; Adrien Chan-Hon-Tong ; Catherine Achard

HIGHLIGHT:      To do this, we propose ActionSpotter, a spotting 
algorithm that takes advantage of Deep Reinforcement Learning to 
efficiently spot actions while adapting its video browsing speed, 
without additional supervision.



24, TITLE:      Roommate Compatibility Detection Through Machine Learning Techniques

http://arxiv.org/abs/2004.06970

AUTHORS:        Mansha Lamba ; Raunak Goswami ; Mr. Vinay ; Mohit Lamba

HIGHLIGHT:      Our objective is to develop an artificially intelligent 
system which aims at checking the compatibility between the roommates of
 same or different sex sharing a common area of residence.



25, TITLE:      Self-Supervised training for blind multi-frame video denoising

http://arxiv.org/abs/2004.06957

AUTHORS:        Dewil Valéry ; Arias Pablo ; Facciolo Gabriele ; Anger Jérémy ; Davy Axel ; Ehret Thibaud

COMMENTS:       14 pages

HIGHLIGHT:      We propose a self-supervised approach for training multi-frame video denoising networks.



26, TITLE:      On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D

http://arxiv.org/abs/2004.06961

AUTHORS:        Geoffrey Pruvost ; Bilel Derbel ; Arnaud Liefooghe ; Ke Li ; Qingfu Zhang

COMMENTS:       European Conference on Evolutionary Computation in Combinatorial Optimization, Apr 2020, Seville, Spain

HIGHLIGHT:      This paper intends to understand and to improve the 
working principle of decomposition-based multi-objective evolutionary 
algorithms.



27, TITLE:      Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations

http://arxiv.org/abs/2004.06965

AUTHORS:        Yu-Syuan Xu ; Shou-Yao Roy Tseng ; Yu Tseng ; Hsien-Kai Kuo ; Yi-Min Tsai

COMMENTS:       CVPR 2020

HIGHLIGHT:      To fulfill this requirement, this paper proposes a 
unified network to accommodate the variations from inter-image 
(cross-image variations) and intra-image (spatial variations).



28, TITLE:      Balancing Training for Multilingual Neural Machine Translation

http://arxiv.org/abs/2004.06748

AUTHORS:        Xinyi Wang ; Yulia Tsvetkov ; Graham Neubig

COMMENTS:       Accepted at ACL 2020

HIGHLIGHT:      In this paper, we propose a method that instead 
automatically learns how to weight training data through a data scorer 
that is optimized to maximize performance on all test languages.



29, TITLE:      Extending Text Informativeness Measures to Passage 
Interestingness Evaluation (Language Model vs. Word Embedding)

http://arxiv.org/abs/2004.06747

AUTHORS:        Carlos-Emiliano González-Gallardo ; Eric SanJuan ; Juan-Manuel Torres-Moreno

HIGHLIGHT:      In this paper we define the concept of Interestingness 
as a generalization of Informativeness, whereby the information need is 
diverse and formalized as an unknown set of implicit queries.



30, TITLE:      A Simple Yet Strong Pipeline for HotpotQA

http://arxiv.org/abs/2004.06753

AUTHORS:        Dirk Groeneveld ; Tushar Khot ;  Mausam ; Ashish Sabharwal

HIGHLIGHT:      Our pipeline has three steps: 1) use BERT to identify 
potentially relevant sentences independently of each other; 2) feed the 
set of selected sentences as context into a standard BERT span 
prediction model to choose an answer; and 3) use the sentence selection 
model, now with the chosen answer, to produce supporting sentences.



31, TITLE:      Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter

http://arxiv.org/abs/2004.06986

AUTHORS:        Philipp Wicke ; Marianna M. Bolognesi

COMMENTS:       41 pages, 6 figures

HIGHLIGHT:      We hereby present an analysis of the discourse around 
#Covid-19, based on a corpus of 200k tweets posted on Twitter during 
March and April 2020.



32, TITLE:      Neural Status Registers

http://arxiv.org/abs/2004.07085

AUTHORS:        Lukas Faber ; Roger Wattenhofer

HIGHLIGHT:      In this paper, we introduce the Neural Status Register (NSR), inspired by physical Status Registers.



33, TITLE:      Seeing Red: PPG Biometrics Using Smartphone Cameras

http://arxiv.org/abs/2004.07088

AUTHORS:        Giulio Lovisotto ; Henry Turner ; Simon Eberz ; Ivan Martinovic

COMMENTS:       8 pages, 15th IEEE Computer Society Workshop on Biometrics 2020

HIGHLIGHT:      In this paper, we propose a system that enables 
photoplethysmogram (PPG)-based authentication by using a smartphone 
camera. We collect a dataset of PPG measurements from a set of 15 users 
over the course of 6-11 sessions per user using an iPhone X for the 
measurements.



34, TITLE:      Analyzing analytical methods: The case of phonology in neural models of spoken language

http://arxiv.org/abs/2004.07070

AUTHORS:        Grzegorz Chrupała ; Bertrand Higy ; Afra Alishahi

COMMENTS:       ACL 2020

HIGHLIGHT:      We use two commonly applied analytical techniques, 
diagnostic classifiers and representational similarity analysis, to 
quantify to what extent neural activation patterns encode phonemes and 
phoneme sequences.



35, TITLE:      Image Segmentation Using Hybrid Representations

http://arxiv.org/abs/2004.07071

AUTHORS:        Alakh Desai ; Ruchi Chauhan ; Jayanthi Sivaswamy

COMMENTS:       4 pages, 6 figures, to be published in ISBI 2020

HIGHLIGHT:      We introduce an end-to-end U-Net based network called 
DU-Net, which uses additional frequency preserving features, namely the 
Scattering Coefficients (SC), for medical image segmentation.



36, TITLE:      lamBERT: Language and Action Learning Using Multimodal BERT

http://arxiv.org/abs/2004.07093

AUTHORS:        Kazuki Miyazawa ; Tatsuya Aoki ; Takato Horii ; Takayuki Nagai

COMMENTS:       8 pages, 9 figures

HIGHLIGHT:      This study proposes the language and action learning 
using multimodal BERT (lamBERT) model that enables the learning of 
language and actions by 1) extending the BERT model to multimodal 
representation and 2) integrating it with reinforcement learning.



37, TITLE:      DeeSCo: Deep heterogeneous ensemble with Stochastic Combinatory loss for gaze estimation

http://arxiv.org/abs/2004.07098

AUTHORS:        Edouard Yvinec ; Arnaud Dapogny ; Kévin Bailly

COMMENTS:       7 pages, 6 figures, FG 2020

HIGHLIGHT:      In this paper, we introduce a deep, end-to-end trainable
 ensemble of heatmap-based weak predictors for 2D/3D gaze estimation.



38, TITLE:      MXR-U-Nets for Real Time Hyperspectral Reconstruction

http://arxiv.org/abs/2004.07003

AUTHORS:        Atmadeep Banerjee ; Akash Palrecha

HIGHLIGHT:      In this paper, we build upon the work of Howard and 
Gugger, He et al. and Misra, D. and propose a CNN architecture that 
accurately reconstructs hyperspectral images from their RGB 
counterparts.



39, TITLE:      Visual Descriptor Learning from Monocular Video

http://arxiv.org/abs/2004.07007

AUTHORS:        Umashankar Deekshith ; Nishit Gajjar ; Max Schwarz ; Sven Behnke

COMMENTS:       International Conference on Computer Vision Theory and Applications (VISAPP) 2020

HIGHLIGHT:      In this paper, we propose a novel way to estimate dense 
correspondence on an RGB image where visual descriptors are learned from
 video examples by training a fully convolutional network.



40, TITLE:      Exploring Probabilistic Soft Logic as a framework for 
integrating top-down and bottom-up processing of language in a task 
context

http://arxiv.org/abs/2004.07000

AUTHORS:        Johannes Dellert

COMMENTS:       32 pages, 2 figures

HIGHLIGHT:      This technical report describes a new prototype 
architecture designed to integrate top-down and bottom-up analysis of 
non-standard linguistic input, where a semantic model of the context of 
an utterance is used to guide the analysis of the non-standard surface 
forms, including their automated normalization in context.



41, TITLE:      FOND Planning for LTLf and PLTLf Goals

http://arxiv.org/abs/2004.07027

AUTHORS:        Francesco Fuggitti

COMMENTS:       Extract of MSc Thesis, 35 pages

HIGHLIGHT:      In this report, we will define a new approach to the 
problem of non deterministic planning for extended temporal goals.



42, TITLE:      Ants can orienteer a thief in their robbery

http://arxiv.org/abs/2004.07017

AUTHORS:        Jonatas B. C. Chagas ; Markus Wagner

HIGHLIGHT:      We propose a two-phase, swarm-intelligence based approach together with a new randomized packing heuristic.



43, TITLE:      Contextual Pyramid Attention Network for Building Segmentation in Aerial Imagery

http://arxiv.org/abs/2004.07018

AUTHORS:        Clint Sebastian ; Raffaele Imbriaco ; Egor Bondarev ; Peter H. N. de With

HIGHLIGHT:      In this work, we propose to improve building 
segmentation of different sizes by capturing long-range dependencies 
using contextual pyramid attention (CPA).



44, TITLE:      Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images

http://arxiv.org/abs/2004.07011

AUTHORS:        Luigi T. Luppino ; Mads A. Hansen ; Michael Kampffmeyer ;
 Filippo M. Bianchi ; Gabriele Moser ; Robert Jenssen ; Stian N. 
Anfinsen

HIGHLIGHT:      We propose to extract relational pixel information 
captured by domain-specific affinity matrices at the input and use this 
to enforce alignment of the code spaces and reduce the impact of change 
pixels on the learning objective.



45, TITLE:      Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

http://arxiv.org/abs/2004.07041

AUTHORS:        David Tellez ; Diederik Hoppener ; Cornelis Verhoef ; 
Dirk Grunhagen ; Pieter Nierop ; Michal Drozdzal ; Jeroen van der Laak ;
 Francesco Ciompi

COMMENTS:       Medical Imaging with Deep Learning 2020 (MIDL20)

HIGHLIGHT:      We propose to train this encoder using supervised multitask learning (MTL) instead.



46, TITLE:      4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid Dynamics

http://arxiv.org/abs/2004.07035

AUTHORS:        Edward Ferdian ; Avan Suinesiaputra ; David Dubowitz ; Debbie Zhao ; Alan Wang ; Brett Cowan ; Alistair Young

COMMENTS:       accepted to Frontiers in Cardiovascular Medicine

HIGHLIGHT:      We utilized computational fluid dynamics simulations to 
generate fluid flow simulations and represent them as synthetic 4D flow 
MRI data. We built our training dataset to mimic actual 4D flow MRI data
 with its corresponding noise distribution.



47, TITLE:      Fully Automated Myocardial Strain Estimation from CMR 
Tagged Images using a Deep Learning Framework in the UK Biobank

http://arxiv.org/abs/2004.07064

AUTHORS:        Edward Ferdian ; Avan Suinesiaputra ; Kenneth Fung ; Nay
 Aung ; Elena Lukaschuk ; Ahmet Barutcu ; Edd Maclean ; Jose Paiva ; 
Stefan K. Piechnik ; Stefan Neubauer ; Steffen E Petersen ; Alistair A. 
Young

COMMENTS:       accepted in Radiology Cardiothoracic Imaging

HIGHLIGHT:      Methods and Materials: In this retrospective 
cross-sectional study, 4508 cases from the UK Biobank were split 
randomly into 3244 training and 812 validation cases, and 452 test 
cases.



48, TITLE:      JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

http://arxiv.org/abs/2004.07054

AUTHORS:        Yu-Huan Wu ; Shang-Hua Gao ; Jie Mei ; Jun Xu ; Deng-Ping Fan ; Chao-Wei Zhao ; Ming-Ming Cheng

COMMENTS:       11 pages, 10 figures

HIGHLIGHT:      In this paper, we develop a novel Joint Classification 
and Segmentation (JCS) system to perform real-time and explainable 
COVID-19 diagnosis. To train our JCS system, we construct a large scale 
COVID-19 Classification and Segmentation (COVID-CS) dataset, with 
144,167 CT images of 400 COVID-19 patients and 350 uninfected cases.



49, TITLE:      Melanoma Detection using Adversarial Training and Deep Transfer Learning

http://arxiv.org/abs/2004.06824

AUTHORS:        Hasib Zunair ; A. Ben Hamza

COMMENTS:       Accepted for publication in the Journal of Physics in Medicine and Biology (PMB), April 2020. Codes at https://github.com/hasibzunair/adversarial-lesions

HIGHLIGHT:      In this paper, we propose a two-stage framework for 
automatic classification of skin lesion images using adversarial 
training and transfer learning toward melanoma detection.



50, TITLE:      A Human Evaluation of AMR-to-English Generation Systems

http://arxiv.org/abs/2004.06814

AUTHORS:        Emma Manning ; Shira Wein ; Nathan Schneider

HIGHLIGHT:      In this work, we present the results of a new human 
evaluation which collects fluency and adequacy scores, as well as 
categorization of error types, for several recent AMR generation 
systems.



51, TITLE:      Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision

http://arxiv.org/abs/2004.06816

AUTHORS:        Hoel Kervadec ; Jose Dolz ; Shanshan Wang ; Eric Granger ; Ismail Ben Ayed

COMMENTS:       Full paper, accepted for presentation at MIDL2020

HIGHLIGHT:      We propose a novel weakly supervised learning 
segmentation based on several global constraints derived from box 
annotations.



52, TITLE:      Intuitive, Interactive Beard and Hair Synthesis with Generative Models

http://arxiv.org/abs/2004.06848

AUTHORS:        Kyle Olszewski ; Duygu Ceylan ; Jun Xing ; Jose Echevarria ; Zhili Chen ; Weikai Chen ; Hao Li

COMMENTS:       To be presented in the 2020 Conference on Computer 
Vision and Pattern Recognition (CVPR 2020, Oral Presentation). 
Supplementary video can be seen at: https://www.youtube.com/watch?v=v4qOtBATrvM

HIGHLIGHT:      We present an interactive approach to synthesizing 
realistic variations in facial hair in images, ranging from subtle edits
 to existing hair to the addition of complex and challenging hair in 
images of clean-shaven subjects.



53, TITLE:      Mosaic Super-resolution via Sequential Feature Pyramid Networks

http://arxiv.org/abs/2004.06853

AUTHORS:        Mehrdad Shoeiby ; Mohammad Ali Armin ; Sadegh Aliakbarian ; Saeed Anwar ; Lars Petersson

COMMENTS:       Accepted by IEEE CVPR Workshop

HIGHLIGHT:      In this paper, we propose to address this limitation by 
introducing a novel method to carry out super-resolution on raw mosaic 
images, multi-spectral or RGB Bayer, captured by modern real-time 
single-shot mosaic sensors.



54, TITLE:      Coreferential Reasoning Learning for Language Representation

http://arxiv.org/abs/2004.06870

AUTHORS:        Deming Ye ; Yankai Lin ; Jiaju Du ; Zhenghao Liu ; Maosong Sun ; Zhiyuan Liu

HIGHLIGHT:      To address this issue, we present CorefBERT, a novel 
language representation model designed to capture the relations between 
noun phrases that co-refer to each other.



55, TITLE:      ToD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogues

http://arxiv.org/abs/2004.06871

AUTHORS:        Chien-Sheng Wu ; Steven Hoi ; Richard Socher ; Caiming Xiong

HIGHLIGHT:      In this work, we combine nine English-based, 
human-human, multi-turn and publicly available task-oriented dialogue 
datasets to conduct language model pre-training.



56, TITLE:      Understanding Aesthetic Evaluation using Deep Learning

http://arxiv.org/abs/2004.06874

AUTHORS:        Jon McCormack ; Andy Lomas

COMMENTS:       Presented at EvoMUSART 2020 Conference

HIGHLIGHT:      In this paper we look at how recent advances in deep 
learning can assist in automating personal aesthetic judgement.



57, TITLE:      Combining Geometric and Information-Theoretic Approaches for Multi-Robot Exploration

http://arxiv.org/abs/2004.06856

AUTHORS:        Aravind Preshant Premkumar ; Kevin Yu ; Pratap Tokekar

HIGHLIGHT:      We present an algorithm to explore an orthogonal polygon using a team of $p$ robots.



58, TITLE:      A Practical Blockchain Framework using Image Hashing for Image Authentication

http://arxiv.org/abs/2004.06860

AUTHORS:        Cameron White ; Manoranjan Paul ; Subrata Chakraborty

COMMENTS:       This is un-published paper

HIGHLIGHT:      This work shows that blockchain can be a suitable 
approach for authenticating images, particularly via image hashing.



59, TITLE:      On the Linguistic Capacity of Real-Time Counter Automata

http://arxiv.org/abs/2004.06866

AUTHORS:        William Merrill

COMMENTS:       Unpublished

HIGHLIGHT:      Counter machines have achieved a newfound relevance to 
the field of natural language processing (NLP): recent work suggests 
some strong-performing recurrent neural networks utilize their memory as
 counters.



60, TITLE:      Human Evaluation of Interpretability: The Case of AI-Generated Music Knowledge

http://arxiv.org/abs/2004.06894

AUTHORS:        Haizi Yu ; Heinrich Taube ; James A. Evans ; Lav R. Varshney

HIGHLIGHT:      Our goal is to reveal both the possibilities and the 
challenges in such a process of decoding expressive messages from AI 
sources.



61, TITLE:      Learning sums of powers of low-degree polynomials in the non-degenerate case

http://arxiv.org/abs/2004.06898

AUTHORS:        Ankit Garg ; Neeraj Kayal ; Chandan Saha

HIGHLIGHT:      In this paper, we give a $\text{poly}((ns)^t)$-time 
learning algorithm for finding the $Q_i$'s given (black-box access to) 
$f$, if the $Q_i's$ satisfy certain non-degeneracy conditions and $n$ is
 larger than $d^2$.



62, TITLE:      Mirror Ritual: Human-Machine Co-Construction of Emotion

http://arxiv.org/abs/2004.06883

AUTHORS:        Nina Rajcic ; Jon McCormack

COMMENTS:       Paper presented at ACM TEI Conference 2020 Arts Track, Sydney Australia

HIGHLIGHT:      Mirror Ritual: Human-Machine Co-Construction of Emotion



63, TITLE:      Effect of Input Noise Dimension in GANs

http://arxiv.org/abs/2004.06882

AUTHORS:        Manisha Padala ; Debojit Das ; Sujit Gujar

HIGHLIGHT:      To overcome the challenges, researchers have proposed 
novel loss functions, architectures, and optimization methods.



64, TITLE:      Analysis of Scoliosis From Spinal X-Ray Images

http://arxiv.org/abs/2004.06887

AUTHORS:        Abdullah-Al-Zubaer Imran ; Chao Huang ; Hui Tang ; Wei 
Fan ; Kenneth M. C. Cheung ; Michael To ; Zhen Qian ; Demetri 
Terzopoulos

COMMENTS:       6 pages, 6 figures, 3 tables

HIGHLIGHT:      Leveraging a carefully-adjusted U-Net model with 
progressive side outputs, we propose an end-to-end segmentation model 
that provides a fully automatic and reliable segmentation of the 
vertebrae associated with scoliosis measurement.







==========Updates to Previous Papers==========

1, TITLE:       MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

http://arxiv.org/abs/2004.02984

AUTHORS:        Zhiqing Sun ; Hongkun Yu ; Xiaodan Song ; Renjie Liu ; Yiming Yang ; Denny Zhou

COMMENTS:       Accepted to ACL 2020

HIGHLIGHT:      In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model.



2, TITLE:       Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation

http://arxiv.org/abs/2004.03892

AUTHORS:        Youyi Song ; Lei Zhu ; Baiying Lei ; Bin Sheng ; Qi Dou ; Jing Qin ; Kup-Sze Choi

COMMENTS:       12 pages and 6 figures

HIGHLIGHT:      In this paper, we present a novel and effective shape 
prior-based approach, called constrained multi-shape evolution, that 
segments all overlapping cytoplasms in the clump simultaneously by 
jointly evolving each cytoplasm's shape guided by the modeled shape 
priors.



3, TITLE:       Capacity, Bandwidth, and Compositionality in Emergent Language Learning

http://arxiv.org/abs/1910.11424

AUTHORS:        Cinjon Resnick ; Abhinav Gupta ; Jakob Foerster ; Andrew M. Dai ; Kyunghyun Cho

COMMENTS:       The first two authors contributed equally. Accepted at AAMAS 2020

HIGHLIGHT:      In this paper, we investigate the learning biases that 
affect the efficacy and compositionality of emergent languages. We 
additionally introduce a set of evaluation metrics with which we analyze
 the learned languages.



4, TITLE:       The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification

http://arxiv.org/abs/2004.06271

AUTHORS:        Pirazh Khorramshahi ; Neehar Peri ; Jun-cheng Chen ; Rama Chellappa

HIGHLIGHT:      In this paper, we present Self-supervised Attention for 
Vehicle Re-identification (SAVER), a novel approach to effectively learn
 vehicle-specific discriminative features.



5, TITLE:       Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods

http://arxiv.org/abs/2002.09247

AUTHORS:        Russa Biswas ; Mehwish Alam ; Harald Sack

HIGHLIGHT:      This paper provides a theoretical analysis and 
comparison of the state-of-the-art alignment methods between two 
embedding spaces representing entity-entity and entity-word.



6, TITLE:       A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

http://arxiv.org/abs/1910.10549

AUTHORS:        Jiahao Lu ; Nataša Sladoje ; Christina Runow Stark ; Eva Darai Ramqvist ; Jan-Michaél Hirsch ; Joakim Lindblad

COMMENTS:       Accepted to ICIAR 2020

HIGHLIGHT:      To facilitate large scale screening, we propose a fully 
automated pipeline for oral cancer detection on whole slide cytology 
images.



7, TITLE:       KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations

http://arxiv.org/abs/2002.12687

AUTHORS:        Yang You ; Yujing Lou ; Chengkun Li ; Zhoujun Cheng ; Liangwei Li ; Lizhuang Ma ; Cewu Lu ; Weiming Wang

COMMENTS:       8 pages; to appear in CVPR 2020

HIGHLIGHT:      To handle the inconsistency between annotations from 
different people, we propose a novel method to aggregate these keypoints
 automatically, through minimization of a fidelity loss.



8, TITLE:       Future Video Synthesis with Object Motion Prediction

http://arxiv.org/abs/2004.00542

AUTHORS:        Yue Wu ; Rongrong Gao ; Jaesik Park ; Qifeng Chen

COMMENTS:       CVPR 2020

HIGHLIGHT:      We present an approach to predict future video frames given a sequence of continuous video frames in the past.



9, TITLE:       Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye

http://arxiv.org/abs/1906.11889

AUTHORS:        Lena A. Jäger ; Silvia Makowski ; Paul Prasse ; Sascha Liehr ; Maximilian Seidler ; Tobias Scheffer

HIGHLIGHT:      We study involuntary micro-movements of the eye for biometric identification.



10, TITLE:      Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

http://arxiv.org/abs/2003.01456

AUTHORS:        Julian Chibane ; Thiemo Alldieck ; Gerard Pons-Moll

COMMENTS:       {IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)2020

HIGHLIGHT:      To solve this, we propose Implicit Feature Networks 
(IF-Nets), which deliver continuous outputs, can handle multiple 
topologies, and complete shapes for missing or sparse input data 
retaining the nice properties of recent learned implicit functions, but 
critically they can also retain detail when it is present in the input 
data, and can reconstruct articulated humans.



11, TITLE:      A Review on Deep Learning Techniques for Video Prediction

http://arxiv.org/abs/2004.05214

AUTHORS:        Sergiu Oprea ; Pablo Martinez-Gonzalez ; Alberto 
Garcia-Garcia ; John Alejandro Castro-Vargas ; Sergio Orts-Escolano ; 
Jose Garcia-Rodriguez ; Antonis Argyros

COMMENTS:       Submitted to TPAMI

HIGHLIGHT:      Motivated by the increasing interest in this task, we 
provide a review on the deep learning methods for prediction in video 
sequences.



12, TITLE:      SoftTriple Loss: Deep Metric Learning Without Triplet Sampling

http://arxiv.org/abs/1909.05235

AUTHORS:        Qi Qian ; Lei Shang ; Baigui Sun ; Juhua Hu ; Hao Li ; Rong Jin

COMMENTS:       accepted by ICCV'19

HIGHLIGHT:      Therefore, we propose the SoftTriple loss to extend the SoftMax loss with multiple centers for each class.



13, TITLE:      TopoTag: A Robust and Scalable Topological Fiducial Marker System

http://arxiv.org/abs/1908.01450

AUTHORS:        Guoxing Yu ; Yongtao Hu ; Jingwen Dai

COMMENTS:       Accepted to TVCG

HIGHLIGHT:      Here we introduce TopoTag, a robust and scalable 
topological fiducial marker system, which supports reliable and accurate
 pose estimation from a single image. We collected a large test dataset 
including in total 169,713 images for evaluation, involving in-plane and
 out-of-plane rotation, image blur, different distances and various 
backgrounds, etc.



14, TITLE:      Classifying Constructive Comments

http://arxiv.org/abs/2004.05476

AUTHORS:        Varada Kolhatkar ; Nithum Thain ; Jeffrey Sorensen ; Lucas Dixon ; Maite Taboada

COMMENTS:       Withdrawn pending a new version

HIGHLIGHT:      We introduce the Constructive Comments Corpus (C3), 
comprised of 12,000 annotated news comments, intended to help build new 
tools for online communities to improve the quality of their 
discussions.



15, TITLE:      Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)

http://arxiv.org/abs/1803.07289

AUTHORS:        Fabian Groh ; Patrick Wieschollek ; Hendrik P. A. Lensch

COMMENTS:       accepted at ACCV 2018

HIGHLIGHT:      In this work, we introduce a natural generalization 
flex-convolution of the conventional convolution layer along with an 
efficient GPU implementation.



16, TITLE:      AI Feynman: a Physics-Inspired Method for Symbolic Regression

http://arxiv.org/abs/1905.11481

AUTHORS:        Silviu-Marian Udrescu ; Max Tegmark

COMMENTS:       15 pages, 2 figs. Our code is available at https://github.com/SJ001/AI-Feynman and our Feynman Symbolic Regression Database for benchmarking can be downloaded at https://space.mit.edu/home/tegmark/aifeynman.html

HIGHLIGHT:      In this spirit, we develop a recursive multidimensional 
symbolic regression algorithm that combines neural network fitting with a
 suite of physics-inspired techniques.



17, TITLE:      InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics

http://arxiv.org/abs/2004.06592

AUTHORS:        Ignacio Serna ; Alejandro Peña ; Aythami Morales ; Julian Fierrez

HIGHLIGHT:      We present a comprehensive analysis of bias effects when
 using an unbalanced training dataset on the features learned by the 
models.



18, TITLE:      Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning

http://arxiv.org/abs/2004.03070

AUTHORS:        Daya Guo ; Akari Asai ; Duyu Tang ; Nan Duan ; Ming Gong ; Linjun Shou ; Daxin Jiang ; Jian Yin ; Ming Zhou

HIGHLIGHT:      In this work, we use multiple knowledge sources as fuels for the model.



19, TITLE:      PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting

http://arxiv.org/abs/2001.05643

AUTHORS:        Saeed Amirgholipour ; Xiangjian He ; Wenjing Jia ; Dadong Wang ; Lei Liu

HIGHLIGHT:      In this paper, we propose a novel Pyramid Density-Aware 
Attention-based network, abbreviated as PDANet, that leverages the 
attention, pyramid scale feature and two branch decoder modules for 
density-aware crowd counting.



20, TITLE:      IoU-aware Single-stage Object Detector for Accurate Localization

http://arxiv.org/abs/1912.05992

AUTHORS:        Shengkai Wu ; Xiaoping Li ; Xinggang Wang

HIGHLIGHT:      In this paper, IoU-aware single-stage object detector is proposed to solve this problem.



21, TITLE:      Quantifying the effects of data augmentation and stain 
color normalization in convolutional neural networks for computational 
pathology

http://arxiv.org/abs/1902.06543

AUTHORS:        David Tellez ; Geert Litjens ; Peter Bandi ; Wouter 
Bulten ; John-Melle Bokhorst ; Francesco Ciompi ; Jeroen van der Laak

COMMENTS:       Accepted in the Medical Image Analysis journal

HIGHLIGHT:      Several techniques have been proposed to reduce the 
generalization error, mainly grouped into two categories: stain color 
augmentation and stain color normalization.



22, TITLE:      Multistage Vertex Cover

http://arxiv.org/abs/1906.00659

AUTHORS:        Till Fluschnik ; Rolf Niedermeier ; Valentin Rohm ; Philipp Zschoche

COMMENTS:       An extended abstract of this paper appeared in Proc. of IPEC'19

HIGHLIGHT:      We show that, different from classic Vertex Cover and 
some other dynamic or temporal variants of it, Multistage Vertex Cover 
is computationally hard even in fairly restricted settings.



23, TITLE:      Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection

http://arxiv.org/abs/2004.04822

AUTHORS:        Zheng Nie ; Jiachen Xu ; Shengchang Zhang

COMMENTS:       10 pages, 30 figures

HIGHLIGHT:      Our methods applied random weighted augmentation to balance different defects types in the training set.



24, TITLE:      Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks

http://arxiv.org/abs/2004.05937

AUTHORS:        Lin Wang ; Kuk-Jin Yoon

COMMENTS:       38 pages, Paper in submission

HIGHLIGHT:      In general, we consider some fundamental questions that 
have been driving this research area and thoroughly generalize the 
research progress and technical details.



25, TITLE:      Optimal Farsighted Agents Tend to Seek Power

http://arxiv.org/abs/1912.01683

AUTHORS:        Alexander Matt Turner ; Logan Smith ; Rohin Shah ; Prasad Tadepalli

COMMENTS:       8 pages, 25 pages with appendices

HIGHLIGHT:      Others have voiced skepticism: humans seem idiosyncratic
 in their urges to power, which need not be present in the agents we 
design.



26, TITLE:      COVID-Net: A Tailored Deep Convolutional Neural Network 
Design for Detection of COVID-19 Cases from Chest X-Ray Images

http://arxiv.org/abs/2003.09871

AUTHORS:        Linda Wang ; Alexander Wong

COMMENTS:       9 pages

HIGHLIGHT:      Therefore, in this study we introduce COVID-Net, a deep 
convolutional neural network design tailored for the detection of 
COVID-19 cases from chest X-ray (CXR) images that is open source and 
available to the general public.



27, TITLE:      Classifier Chains: A Review and Perspectives

http://arxiv.org/abs/1912.13405

AUTHORS:        Jesse Read ; Bernhard Pfahringer ; Geoff Holmes ; Eibe Frank

HIGHLIGHT:      Given this past and ongoing interest, which covers a 
broad range of applications and research themes, the goal of this work 
is to provide a review of classifier chains, a survey of the techniques 
and extensions provided in the literature, as well as perspectives for 
this approach in the domain of multi-label classification in the future.



28, TITLE:      Computational Complexity of the Hylland-Zeckhauser Scheme for One-Sided Matching Markets

http://arxiv.org/abs/2004.01348

AUTHORS:        Vijay V. Vazirani ; Mihalis Yannakakis

COMMENTS:       22 pages

HIGHLIGHT:      We present the following partial resolution: 1.



29, TITLE:      A negative case analysis of visual grounding methods for VQA

http://arxiv.org/abs/2004.05704

AUTHORS:        Robik Shrestha ; Kushal Kafle ; Christopher Kanan

HIGHLIGHT:      To address this issue, recent bias mitigation methods 
for VQA propose to incorporate visual cues (e.g., human attention maps) 
to better ground the VQA models, showcasing impressive gains.



30, TITLE:      Crafty: Efficient, HTM-Compatible Persistent Transactions

http://arxiv.org/abs/2004.00262

AUTHORS:        Kaan Genç ; Michael D. Bond ; Guoqing Harry Xu

COMMENTS:       32 pages, 24 figures. To appear in PLDI 2020

HIGHLIGHT:      This paper introduces Crafty, a new approach for 
ensuring consistency and atomicity on persistent memory operations using
 commodity hardware with existing hardware transactional memory (HTM) 
capabilities, while incurring low overhead.



31, TITLE:      Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

http://arxiv.org/abs/1806.06055

AUTHORS:        L. Elisa Celis ; Lingxiao Huang ; Vijay Keswani ; Nisheeth K. Vishnoi

HIGHLIGHT:      The main contribution of this paper is a new 
meta-algorithm for classification that takes as input a large class of 
fairness constraints, with respect to multiple non-disjoint sensitive 
attributes, and which comes with provable guarantees.



32, TITLE:      Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

http://arxiv.org/abs/1909.13817

AUTHORS:        Thanh Huy Nguyen ; Sylvie Daniel ; Didier Gueriot ; Christophe Sintes ; Jean-Marc Le Caillec

COMMENTS:       20 pages, 15 figures. Accepted to be published in IEEE JSTARS

HIGHLIGHT:      This paper presents a coarse-to-fine registration method
 of aerial/satellite optical imagery with airborne LiDAR data acquired 
in such context.



33, TITLE:      Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

http://arxiv.org/abs/2004.06165

AUTHORS:        Xiujun Li ; Xi Yin ; Chunyuan Li ; Pengchuan Zhang ; 
Xiaowei Hu ; Lei Zhang ; Lijuan Wang ; Houdong Hu ; Li Dong ; Furu Wei ;
 Yejin Choi ; Jianfeng Gao

HIGHLIGHT:      Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks



34, TITLE:      Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image

http://arxiv.org/abs/1906.07480

AUTHORS:        Matthieu Grard ; Emmanuel Dellandréa ; Liming Chen

COMMENTS:       Published in International Journal of Computer Vision, 2020

HIGHLIGHT:      We therefore propose a multicameral design composed of 
subtask-specific lightweight decoder and encoder-decoder units, coupled 
in cascade to encourage subtask-specific feature reuse and enforce a 
learning path within the decoding process. We thus also introduce a 
synthetic dataset of dense homogeneous object layouts, namely Mikado, 
which extensibly contains more instances and inter-instance occlusions 
per image than these public datasets.



35, TITLE:      Image Captioning using Facial Expression and Attention

http://arxiv.org/abs/1908.02923

AUTHORS:        Omid Mohamad Nezami ; Mark Dras ; Stephen Wan ; Cecile Paris

HIGHLIGHT:      This paper addresses this issue by proposing novel image
 captioning models which use facial expression features to generate 
image captions.



36, TITLE:      PoKi: A Large Dataset of Poems by Children

http://arxiv.org/abs/2004.06188

AUTHORS:        Will E. Hipson ; Saif M. Mohammad

HIGHLIGHT:      We present a new corpus of child-written text, PoKi, 
which includes about 62 thousand poems written by children from grades 1
 to 12.



37, TITLE:      Learning a Directional Soft Lane Affordance Model for Road Scenes Using Self-Supervision

http://arxiv.org/abs/2002.11477

AUTHORS:        Robin Karlsson ; Erik Sjoberg

COMMENTS:       Accepted for IEEE IV 2020

HIGHLIGHT:      This work proposes a novel self-supervised method for 
training a probabilistic network model to estimate the regions humans 
are most likely to drive in as well as a multimodal representation of 
the inferred direction of travel at each point.



38, TITLE:      Fast Robot Arm Inverse Kinematics and Path Planning Under Complex Static and Dynamic Obstacle Constraints

http://arxiv.org/abs/1906.10678

AUTHORS:        David W. Arathorn

HIGHLIGHT:      Fast Robot Arm Inverse Kinematics and Path Planning Under Complex Static and Dynamic Obstacle Constraints



39, TITLE:      Resilient Coverage: Exploring the Local-to-Global Trade-off

http://arxiv.org/abs/1910.01917

AUTHORS:        Ragesh K. Ramachandran ; Lifeng Zhou James A. Preiss ; Gaurav S. Sukhatme

COMMENTS:       8 pages, 5 figures, submitted to IROS 2020

HIGHLIGHT:      We propose a centralized control framework to select 
suitable robots from a heterogeneous pool and place them at appropriate 
locations to monitor a region for events of interest.



40, TITLE:      A Simple Transfer Learning Baseline for Ellipsis Resolution

http://arxiv.org/abs/1908.11141

AUTHORS:        Rahul Aralikatte ; Matthew Lamm ; Daniel Hardt ; Anders Søgaard

COMMENTS:       Preprint. Work in progress

HIGHLIGHT:      We present a strong baseline for English ellipsis 
resolution that exploits this similarity by relying on architectures 
developed for machine reading comprehension.



41, TITLE:      Towards Automatic Generation of Questions from Long Answers

http://arxiv.org/abs/2004.05109

AUTHORS:        Shlok Kumar Mishra ; Pranav Goel ; Abhishek Sharma ; Abhyuday Jagannatha ; David Jacobs ; Hal Daumé III

HIGHLIGHT:      Therefore, we propose a novel evaluation benchmark to 
assess the performance of existing AQG systems for long-text answers.



42, TITLE:      JGAN: A Joint Formulation of GAN for Synthesizing Images and Labels

http://arxiv.org/abs/1905.11574

AUTHORS:        Minje Park

HIGHLIGHT:      In this paper, we provide an alternative formulation of 
GAN which models joint distribution of images and labels.



43, TITLE:      End-to-End Variational Networks for Accelerated MRI Reconstruction

http://arxiv.org/abs/2004.06688

AUTHORS:        Anuroop Sriram ; Jure Zbontar ; Tullie Murrell ; Aaron 
Defazio ; C. Lawrence Zitnick ; Nafissa Yakubova ; Florian Knoll ; 
Patricia Johnson

HIGHLIGHT:      In this paper, we present a new approach to this problem
 that extends previously proposed variational methods by learning fully 
end-to-end.



44, TITLE:      The role of formalism in system requirements (extended version)

http://arxiv.org/abs/1911.02564

AUTHORS:        Jean-Michel Bruel ; Sophie Ebersold ; Florian Galinier ; Alexandr Naumchev ; Manuel Mazzara ; Bertrand Meyer

COMMENTS:       Fourth version (15 April 2020). This is the full 
version, including some sections and 2 appendices not appearing in the 
short version

HIGHLIGHT:      It presents approaches in all of these categories, 
altogether 22 different ones, including for example SysML, Relax, 
Eiffel, Event-B, Alloy.



45, TITLE:      Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data

http://arxiv.org/abs/1912.07669

AUTHORS:        Burhaneddin Yaman ; Seyed Amir Hossein Hosseini ; Steen 
Moeller ; Jutta Ellermann ; Kâmil Uğurbil ; Mehmet Akçakaya

COMMENTS:       This work is an extension of our previous work arXiv:1910.09116

HIGHLIGHT:      Results: Results on five different knee sequences at 
acceleration rate of 4 shows that proposed self-supervised approach 
performs closely with supervised learning, while significantly 
outperforming conventional compressed sensing and parallel imaging, as 
characterized by quantitative metrics and a clinical reader study.



46, TITLE:      Deep Learning Models for Multilingual Hate Speech Detection

http://arxiv.org/abs/2004.06465

AUTHORS:        Sai Saketh Aluru ; Binny Mathew ; Punyajoy Saha ; Animesh Mukherjee

COMMENTS:       16 pages

HIGHLIGHT:      In this paper, we conduct a large scale analysis of 
multilingual hate speech in 9 languages from 16 different sources.



47, TITLE:      Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima

http://arxiv.org/abs/1905.10027

AUTHORS:        Qi Cai ; Zhuoran Yang ; Jason D. Lee ; Zhaoran Wang

HIGHLIGHT:      In this paper, we prove for the first time that neural 
TD converges at a sublinear rate to the global optimum of the 
mean-squared projected Bellman error for policy evaluation.



48, TITLE:      Self6D: Self-Supervised Monocular 6D Object Pose Estimation

http://arxiv.org/abs/2004.06468

AUTHORS:        Gu Wang ; Fabian Manhardt ; Jianzhun Shao ; Xiangyang Ji ; Nassir Navab ; Federico Tombari

COMMENTS:       fix references for appendix

HIGHLIGHT:      To overcome this shortcoming, we propose the idea of 
monocular 6D pose estimation by means of self-supervised learning, which
 eradicates the need for real data with annotations.



49, TITLE:      Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks

http://arxiv.org/abs/1902.00972

AUTHORS:        Jenna Kanerva ; Filip Ginter ; Tapio Salakoski

COMMENTS:       Accepted to the Journal of Natural Language Engineering

HIGHLIGHT:      In this paper we present a novel lemmatization method 
based on a sequence-to-sequence neural network architecture and 
morphosyntactic context representation.



50, TITLE:      Neural Image Compression for Gigapixel Histopathology Image Analysis

http://arxiv.org/abs/1811.02840

AUTHORS:        David Tellez ; Geert Litjens ; Jeroen van der Laak ; Francesco Ciompi

COMMENTS:       Accepted in the IEEE Transactions on Pattern Analysis and Machine Intelligence journal

HIGHLIGHT:      We propose Neural Image Compression (NIC), a two-step 
method to build convolutional neural networks for gigapixel image 
analysis solely using weak image-level labels.



51, TITLE:      Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

http://arxiv.org/abs/2003.09085

AUTHORS:        Jakaria Rabbi ; Nilanjan Ray ; Matthias Schubert ; Subir Chowdhury ; Dennis Chao

COMMENTS:       This paper contains 27 pages and submitted to MDPI remote sensing journal (under review) GitHub Repository: https://github.com/Jakaria08/EESRGAN (Implementation)

HIGHLIGHT:      We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network.



52, TITLE:      Efficiently and Effectively Recognizing Toricity of Steady State Varieties

http://arxiv.org/abs/1910.04100

AUTHORS:        Dima Grigoriev ; Alexandru Iosif ; Hamid Rahkooy ; Thomas Sturm ; Andreas Weber

COMMENTS:       We made the presentation clearer and fixed many small 
flaws and typos. A database with our computations is now available as 
ancillary file

HIGHLIGHT:      We present algorithms and computations on 129 models 
from the BioModels repository testing for group and coset structures 
over both the complex numbers and the real numbers.



53, TITLE:      On Optimal Transformer Depth for Low-Resource Language Translation

http://arxiv.org/abs/2004.04418

AUTHORS:        Elan van Biljon ; Arnu Pretorius ; Julia Kreutzer

HIGHLIGHT:      In this work, we find that the current trend in the 
field to use very large models is detrimental for low-resource 
languages, since it makes training more difficult and hurts overall 
performance, confirming previous observations.



54, TITLE:      Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions

http://arxiv.org/abs/1904.04205

AUTHORS:        Hoel Kervadec ; Jose Dolz ; Jing Yuan ; Christian Desrosiers ; Eric Granger ; Ismail Ben Ayed

HIGHLIGHT:      We propose log-barrier extensions, which approximate 
Lagrangian optimization of constrained-CNN problems with a sequence of 
unconstrained losses.