2020.04.13.txt

==========New Papers==========
1, TITLE:       6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
http://arxiv.org/abs/2004.04807
AUTHORS:        Mai Bui ; Tolga Birdal ; Haowen Deng ; Shadi Albarqouni ; Leonidas Guibas ; Slobodan Ilic ; Nassir Navab
COMMENTS:       project page under https://multimodal3dvision.github.io
HIGHLIGHT:      We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses. We introduce a new dataset specifically designed to foster camera localization research in ambiguous environments and exhaustively evaluate our method on synthetic as well as real data on both ambiguous scenes and on non-ambiguous benchmark datasets.

2, TITLE:       Phase Consistent Ecological Domain Adaptation
http://arxiv.org/abs/2004.04923
AUTHORS:        Yanchao Yang ; Dong Lao ; Ganesh Sundaramoorthi ; Stefano Soatto
HIGHLIGHT:      We introduce two criteria to regularize the optimization involved in learning a classifier in a domain where no annotated data are available, leveraging annotated data in a different domain, a problem known as unsupervised domain adaptation.

3, TITLE:       FST Morphology for the Endangered Skolt Sami Language
http://arxiv.org/abs/2004.04803
AUTHORS:        Jack Rueter ; Mika Hämäläinen
COMMENTS:       Accepted to The 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020)
HIGHLIGHT:      We present advances in the development of a FST-based morphological analyzer and generator for Skolt Sami.

4, TITLE:       Multimodal Categorization of Crisis Events in Social Media
http://arxiv.org/abs/2004.04917
AUTHORS:        Mahdi Abavisani ; Liwei Wu ; Shengli Hu ; Joel Tetreault ; Alejandro Jaimes
COMMENTS:       Conference on Computer Vision and Pattern Recognition (CVPR 2020)
HIGHLIGHT:      In this paper, we present a new multimodal fusion method that leverages both images and texts as input.

5, TITLE:       A Simple Method for Computing Some Pseudo-Elliptic Integrals in Terms of Elementary Functions
http://arxiv.org/abs/2004.04910
AUTHORS:        Sam Blake
HIGHLIGHT:      We introduce a method for computing some pseudo-elliptic integrals in terms of elementary functions.

6, TITLE:       Person Re-Identification via Active Hard Sample Mining
http://arxiv.org/abs/2004.04912
AUTHORS:        Xin Xu ; Lei Liu ; Weifeng Liu ; Meng Wang ; Ruimin Hu
HIGHLIGHT:      To alleviate such a problem, we present an active hard sample mining framework via training an effective re-ID model with the least labeling efforts.

7, TITLE:       Analyze and Development System with Multiple Biometric Identification
http://arxiv.org/abs/2004.04911
AUTHORS:        Sher Dadakhanov
COMMENTS:       Multiple Biometric Identification
HIGHLIGHT:      In order to increase safety in 2005, biometric identification methods were developed government and business sectors, but today it has reached almost all private sectors as Banking, Finance, home security and protection, healthcare, business security and security etc.

8, TITLE:       Deep Residual Correction Network for Partial Domain Adaptation
http://arxiv.org/abs/2004.04914
AUTHORS:        Shuang Li ; Chi Harold Liu ; Qiuxia Lin ; Qi Wen ; Limin Su ; Gao Huang ; Zhengming Ding
COMMENTS:       Accepted by T-PAMI, 2020
HIGHLIGHT:      This paper proposes an efficiently-implemented Deep Residual Correction Network (DRCN) by plugging one residual block into the source network along with the task-specific feature layer, which effectively enhances the adaptation from source to target and explicitly weakens the influence from the irrelevant source classes.

9, TITLE:       State-Relabeling Adversarial Active Learning
http://arxiv.org/abs/2004.04943
AUTHORS:        Beichen Zhang ; Liang Li ; Shijie Yang ; Shuhui Wang ; Zheng-Jun Zha ; Qingming Huang
COMMENTS:       Accepted as Oral at CVPR 2020
HIGHLIGHT:      In this paper, we propose a state relabeling adversarial active learning model (SRAAL), that leverages both the annotation and the labeled/unlabeled state information for deriving the most informative unlabeled samples.

10, 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.

11, TITLE:      The Effect of Sociocultural Variables on Sarcasm Communication Online
http://arxiv.org/abs/2004.04945
AUTHORS:        Silviu Vlad Oprea ; Walid Magdy
COMMENTS:       Accepted as a full paper at CSCW 2020. Please cite the CSCW version
HIGHLIGHT:      In this paper we fill this gap by performing a quantitative analysis on the influence of sociocultural variables, including gender, age, country, and English language nativeness, on the effectiveness of sarcastic communication online.

12, TITLE:      Scalable Multilingual Frontend for TTS
http://arxiv.org/abs/2004.04934
AUTHORS:        Alistair Conkie ; Andrew Finch
COMMENTS:       To appear in IEEE ICASSP 2020
HIGHLIGHT:      This paper describes progress towards making a Neural Text-to-Speech (TTS) Frontend that works for many languages and can be easily extended to new languages.

13, TITLE:      Real-world Person Re-Identification via Degradation Invariance Learning
http://arxiv.org/abs/2004.04933
AUTHORS:        Yukun Huang ; Zheng-Jun Zha ; Xueyang Fu ; Richang Hong ; Liang Li
COMMENTS:       To appear in CVPR2020
HIGHLIGHT:      In this paper, to solve the above problem, we propose a degradation invariance learning framework for real-world person Re-ID.

14, TITLE:      Identifying Cultural Differences through Multi-Lingual Wikipedia
http://arxiv.org/abs/2004.04938
AUTHORS:        Yufei Tian ; Tuhin Chakrabarty ; Fred Morstatter ; Nanyun Peng
HIGHLIGHT:      We present a computational approach to learn cultural models that encode the general opinions and values of cultures from multi-lingual Wikipedia.

15, TITLE:      ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection
http://arxiv.org/abs/2004.04940
AUTHORS:        Yuxin Wang ; Hongtao Xie ; Zhengjun Zha ; Mengting Xing ; Zilong Fu ; Yongdong Zhang
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this paper, we propose the ContourNet, which effectively handles these two problems taking a further step toward accurate arbitrary-shaped text detection.

16, TITLE:      Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
http://arxiv.org/abs/2004.04968
AUTHORS:        Hirokatsu Kataoka ; Tenga Wakamiya ; Kensho Hara ; Yutaka Satoh
COMMENTS:       Codes and pre-trained models are publicly available: https://github.com/kenshohara/3D-ResNets-PyTorch
HIGHLIGHT:      Therefore, in the present paper, we conduct exploration study in order to improve spatiotemporal 3D CNNs as follows: (i) Recently proposed large-scale video datasets help improve spatiotemporal 3D CNNs in terms of video classification accuracy. How can we collect and use a video dataset to further improve spatiotemporal 3D Convolutional Neural Networks (3D CNNs)?

17, TITLE:      Natural Perturbation for Robust Question Answering
http://arxiv.org/abs/2004.04849
AUTHORS:        Daniel Khashabi ; Tushar Khot ; Ashish Sabharwal
HIGHLIGHT:      As an alternative to the standard approach of addressing this issue by constructing training sets of completely new examples, we propose doing so via minimal perturbation of examples.

18, TITLE:      Generating Multilingual Voices Using Speaker Space Translation Based on Bilingual Speaker Data
http://arxiv.org/abs/2004.04972
AUTHORS:        Soumi Maiti ; Erik Marchi ; Alistair Conkie
COMMENTS:       Accepted to IEEE ICASSP 2020
HIGHLIGHT:      We present progress towards bilingual Text-to-Speech which is able to transform a monolingual voice to speak a second language while preserving speaker voice quality.

19, TITLE:      Spatial Priming for Detecting Human-Object Interactions
http://arxiv.org/abs/2004.04851
AUTHORS:        Ankan Bansal ; Sai Saketh Rambhatla ; Abhinav Shrivastava ; Rama Chellappa
HIGHLIGHT:      In this paper, we present a method for exploiting this spatial layout information for detecting HOIs in images.

20, TITLE:      Predictable Accelerator Design with Time-Sensitive Affine Types
http://arxiv.org/abs/2004.04852
AUTHORS:        Rachit Nigam ; Sachille Atapattu ; Samuel Thomas ; Zhijing Li ; Theodore Bauer ; Yuwei Ye ; Apurva Koti ; Adrian Sampson ; Zhiru Zhang
COMMENTS:       Camera-ready paper accepted to PLDI 2020
HIGHLIGHT:      This paper proposes a type system that restricts HLS to programs that can predictably compile to hardware accelerators.

21, TITLE:      Learning to Visually Navigate in Photorealistic Environments Without any Supervision
http://arxiv.org/abs/2004.04954
AUTHORS:        Lina Mezghani ; Sainbayar Sukhbaatar ; Arthur Szlam ; Armand Joulin ; Piotr Bojanowski
HIGHLIGHT:      In this paper, we introduce a novel approach for learning to navigate from image inputs without external supervision or reward.

22, TITLE:      Boosting Semantic Human Matting with Coarse Annotations
http://arxiv.org/abs/2004.04955
AUTHORS:        Jinlin Liu ; Yuan Yao ; Wendi Hou ; Miaomiao Cui ; Xuansong Xie ; Changshui Zhang ; Xian-sheng Hua
HIGHLIGHT:      In this paper, we propose to use coarse annotated data coupled with fine annotated data to boost end-to-end semantic human matting without trimaps as extra input.

23, TITLE:      Rephrasing visual questions by specifying the entropy of the answer distribution
http://arxiv.org/abs/2004.04963
AUTHORS:        Kento Terao ; Toru Tamaki ; Bisser Raytchev ; Kazufumi Kaneda ; Shun'ichi Satoh
COMMENTS:       10 pages
HIGHLIGHT:      We propose two learning strategies to train the proposed model with the VQA v2 dataset, which has no ambiguity information.

24, TITLE:      3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds
http://arxiv.org/abs/2004.04962
AUTHORS:        Jiale Li ; Shujie Luo ; Ziqi Zhu ; Hang Dai ; Andrey S. Krylov ; Yong Ding ; Ling Shao
COMMENTS:       11 pages, 9 figures
HIGHLIGHT:      In this paper, we add a 3D IoU prediction branch to the regular classification and regression branches.

25, TITLE:      Automated Spelling Correction for Clinical Text Mining in Russian
http://arxiv.org/abs/2004.04987
AUTHORS:        Ksenia Balabaeva ; Anastasia Funkner ; Sergey Kovalchuk
COMMENTS:       This paper is accepted for publication to MIE 2020 Conference
HIGHLIGHT:      The main goal of this paper is to develop a spell checker module for clinical text in Russian.

26, TITLE:      Latent regularization for feature selection using kernel methods in tumor classification
http://arxiv.org/abs/2004.04866
AUTHORS:        Martin Palazzo ; Patricio Yankilevich ; Pierre Beauseroy
HIGHLIGHT:      In this work we propose a feature selection method based on Multiple Kernel Learning that results in a reduced subset of genes and a custom kernel that improves the classification performance when used in support vector classification.

27, TITLE:      Improved Residual Networks for Image and Video Recognition
http://arxiv.org/abs/2004.04989
AUTHORS:        Ionut Cosmin Duta ; Li Liu ; Fan Zhu ; Ling Shao
HIGHLIGHT:      In this work we propose an improved version of ResNets.

28, TITLE:      MRQy: An Open-Source Tool for Quality Control of MR Imaging Data
http://arxiv.org/abs/2004.04871
AUTHORS:        Amir Reza Sadri ; Andrew Janowczyk ; Ren Zhou ; Ruchika Verma ; Jacob Antunes ; Anant Madabhushi ; Pallavi Tiwari ; Satish E. Viswanath
COMMENTS:       23 pages, 7 figures. Submitted to Medical Physics
HIGHLIGHT:      We present MRQy, a new open-source quality control tool to (a) interrogate MRI cohorts for site- or equipment-based differences, and (b) quantify the impact of MRI artifacts on relative image quality; to help determine how to correct for these variations prior to model development.

29, TITLE:      Robust Line Segments Matching via Graph Convolution Networks
http://arxiv.org/abs/2004.04993
AUTHORS:        QuanMeng Ma ; Guang Jiang ; DianZhi Lai
HIGHLIGHT:      In this paper, we present a new method of using a graph convolution network to match line segments in a pair of images, and we design a graph-based strategy of matching line segments with relaxing to an optimal transport problem.

30, TITLE:      A Simplified Run Time Analysis of the Univariate Marginal Distribution Algorithm on LeadingOnes
http://arxiv.org/abs/2004.04978
AUTHORS:        Benjamin Doerr ; Martin Krejca
HIGHLIGHT:      With elementary means, we prove a stronger run time guarantee for the univariate marginal distribution algorithm (UMDA) optimizing the LeadingOnes benchmark function in the desirable regime with low genetic drift.

31, TITLE:      SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects
http://arxiv.org/abs/2004.04977
AUTHORS:        Evangelos Ntavelis ; Andrés Romero ; Iason Kastanis ; Luc Van Gool ; Radu Timofte
HIGHLIGHT:      To address these limitations, we propose SESAME, a novel generator-discriminator pair for Semantic Editing of Scenes by Adding, Manipulating or Erasing objects.

32, TITLE:      Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos
http://arxiv.org/abs/2004.04979
AUTHORS:        Jiawei Liu ; Xierong Zhu ; Zheng-Jun Zha ; Na Jiang
HIGHLIGHT:      In this work, we propose a novel Co-Saliency Spatio-Temporal Interaction Network (CSTNet) for person re-identification in videos.

33, TITLE:      Spatiotemporal Fusion in 3D CNNs: A Probabilistic View
http://arxiv.org/abs/2004.04981
AUTHORS:        Yizhou Zhou ; Xiaoyan Sun ; Chong Luo ; Zheng-Jun Zha ; Wenjun Zeng
COMMENTS:       To be published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
HIGHLIGHT:      In this paper, we propose to convert the spatiotemporal fusion strategies into a probability space, which allows us to perform network-level evaluations of various fusion strategies without having to train them separately.

34, TITLE:      Negation Detection for Clinical Text Mining in Russian
http://arxiv.org/abs/2004.04980
AUTHORS:        Anastasia Funkner ; Ksenia Balabaeva ; Sergey Kovalchuk
COMMENTS:       5 pages, 1 figure, 3 tables, accepted for the conference MIE 2020
HIGHLIGHT:      This paper is devoted to a module of negation detection.

35, TITLE:      Overestimation of Syntactic Representationin Neural Language Models
http://arxiv.org/abs/2004.05067
AUTHORS:        Jordan Kodner ; Nitish Gupta
COMMENTS:       Accepted for publication at ACL 2020
HIGHLIGHT:      Overestimation of Syntactic Representationin Neural Language Models

36, TITLE:      Uncrowded Hypervolume-based Multi-objective Optimization with Gene-pool Optimal Mixing
http://arxiv.org/abs/2004.05068
AUTHORS:        S. C. Maree ; T. Alderliesten ; P. A. N. Bosman
HIGHLIGHT:      We use the state-of-the-art gene-pool optimal mixing evolutionary algorithm (GOMEA) that is capable of efficiently exploiting the intrinsically available grey-box properties of this problem.

37, TITLE:      A New Dataset for Natural Language Inference from Code-mixed Conversations
http://arxiv.org/abs/2004.05051
AUTHORS:        Simran Khanuja ; Sandipan Dandapat ; Sunayana Sitaram ; Monojit Choudhury
COMMENTS:       To appear in CALCS, LREC 2020
HIGHLIGHT:      In this paper, we present the first dataset for code-mixed NLI, in which both the premises and hypotheses are in code-mixed Hindi-English.

38, TITLE:      ASL Recognition with Metric-Learning based Lightweight Network
http://arxiv.org/abs/2004.05054
AUTHORS:        Evgeny Izutov
HIGHLIGHT:      We make a step in that direction by proposing a lightweight network for ASL gesture recognition with a performance sufficient for practical applications.

39, TITLE:      Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
http://arxiv.org/abs/2004.05080
AUTHORS:        Jiaqi Li ; Ming Liu ; Min-Yen Kan ; Zihao Zheng ; Zekun Wang ; Wenqiang Lei ; Ting Liu ; Bing Qin
HIGHLIGHT:      We present the Molweni dataset, a machine reading comprehension (MRC) dataset built over multiparty dialogues.

40, TITLE:      CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning
http://arxiv.org/abs/2004.05077
AUTHORS:        Janderson Ferreira ; Agostinho A. F. Júnior ; Yves M. Galvão ; Bruno J. T. Fernandes ; Pablo Barros
HIGHLIGHT:      To measure the efficiency of our solution, we propose a database with different scenarios of motion planning problems.

41, TITLE:      Socioeconomic correlations of urban patterns inferred from aerial images: interpreting activation maps of Convolutional Neural Networks
http://arxiv.org/abs/2004.04907
AUTHORS:        Jacob Levy Abitbol ; Márton Karsai
COMMENTS:       19 pages, 17 figures
HIGHLIGHT:      We show that the model disregards the spatial correlations existing between urban class and socioeconomic status to derive its predictions.

42, TITLE:      Dense Passage Retrieval for Open-Domain Question Answering
http://arxiv.org/abs/2004.04906
AUTHORS:        Vladimir Karpukhin ; Barlas Oğuz ; Sewon Min ; Ledell Wu ; Sergey Edunov ; Danqi Chen ; Wen-tau Yih
HIGHLIGHT:      In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework.

43, TITLE:      Designing Precise and Robust Dialogue Response Evaluators
http://arxiv.org/abs/2004.04908
AUTHORS:        Tianyu Zhao ; Divesh Lala ; Tatsuya Kawahara
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      In this work, we propose to build a reference-free evaluator and exploit the power of semi-supervised training and pretrained (masked) language models.

44, TITLE:      An In-depth Walkthrough on Evolution of Neural Machine Translation
http://arxiv.org/abs/2004.04902
AUTHORS:        Rohan Jagtap ; Dr. Sudhir N. Dhage
COMMENTS:       10 pages, 10 figures
HIGHLIGHT:      This paper aims to study the major trends in Neural Machine Translation, the state of the art models in the domain and a high level comparison between them.

45, TITLE:      MA 3 : Model Agnostic Adversarial Augmentation for Few Shot learning
http://arxiv.org/abs/2004.05100
AUTHORS:        Rohit Jena ; Shirsendu Sukanta Halder ; Katia Sycara
COMMENTS:       Accepted at CVPR Workshop on Visual Learning with Limited Labels 2020
HIGHLIGHT:      In this paper, we explore the domain of few-shot learning with a novel augmentation technique.

46, TITLE:      Resources: A Safe Language Abstraction for Money
http://arxiv.org/abs/2004.05106
AUTHORS:        Sam Blackshear ; David L. Dill ; Shaz Qadeer ; Clark W. Barrett ; John C. Mitchell ; Oded Padon ; Yoni Zohar
COMMENTS:       14 pages, 7 figures
HIGHLIGHT:      Addressing this need, we present flexible and reliable abstractions for programming with digital currency in the Move language.

47, TITLE:      SimpleTran: Transferring Pre-Trained Sentence Embeddings for Low Resource Text Classification
http://arxiv.org/abs/2004.05119
AUTHORS:        Siddhant Garg ; Rohit Kumar Sharma ; Yingyu Liang
HIGHLIGHT:      We propose an alternative transfer learning approach called SimpleTran which is simple and effective for low resource text classification characterized by small sized datasets.

48, TITLE:      Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric
http://arxiv.org/abs/2004.05001
AUTHORS:        Ivan Yamshchikov ; Viacheslav Shibaev ; Nikolay Khlebnikov ; Alexey Tikhonov
HIGHLIGHT:      This paper provides a comprehensive analysis for more than a dozen of such methods.

49, TITLE:      Self Punishment and Reward Backfill for Deep Q-Learning
http://arxiv.org/abs/2004.05002
AUTHORS:        Mohammad Reza Bonyadi ; Rui Wang ; Maryam Ziaei
HIGHLIGHT:      In this paper, we propose two strategies, inspired by behavioural psychology, to estimate a more informative reward value for actions with no reward.

50, TITLE:      Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned
http://arxiv.org/abs/2004.05125
AUTHORS:        Edwin Zhang ; Nikhil Gupta ; Rodrigo Nogueira ; Kyunghyun Cho ; Jimmy Lin
HIGHLIGHT:      We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI.

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

52, TITLE:      Deep transfer learning for improving single-EEG arousal detection
http://arxiv.org/abs/2004.05111
AUTHORS:        Alexander Neergaard Olesen ; Poul Jennum ; Emmanuel Mignot ; Helge Bjarup Dissing Sorensen
HIGHLIGHT:      Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data.

53, TITLE:      Colouring $(sP_1+P_5)$-Free Graphs: a Mim-Width Perspective
http://arxiv.org/abs/2004.05022
AUTHORS:        Nick Brettell ; Jake Horsfield ; Daniel Paulusma
HIGHLIGHT:      For this problem, we may assume that the input graph is $K_{k+1}$-free.

54, TITLE:      Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service
http://arxiv.org/abs/2004.05146
AUTHORS:        Amutheezan Sivagnanam ; Afiya Ayman ; Michael Wilbur ; Philip Pugliese ; Abhishek Dubey ; Aron Laszka
HIGHLIGHT:      We present an integer program for optimal discrete-time scheduling, and we propose polynomial-time heuristic algorithms and a genetic algorithm for finding solutions for larger networks.

55, TITLE:      Weakly supervised multiple instance learning histopathological tumor segmentation
http://arxiv.org/abs/2004.05024
AUTHORS:        Marvin Lerousseau ; Maria Vakalopoulou ; Marion Classe ; Julien Adam ; Enzo Battistella ; Alexandre Carré ; Théo Estienne ; Théophraste Henry ; Eric Deutsch ; Nikos Paragios
COMMENTS:       10 pages, 3 figures
HIGHLIGHT:      To this end, in this paper, we propose a weakly supervised framework relying on weak standard clinical practice annotations, available in most medical centers.

56, TITLE:      One Model to Recognize Them All: Marginal Distillation from NER Models with Different Tag Sets
http://arxiv.org/abs/2004.05140
AUTHORS:        Keunwoo Peter Yu ; Yi Yang
HIGHLIGHT:      This paper presents a marginal distillation (MARDI) approach for training a unified NER model from resources with disjoint or heterogeneous tag sets.

57, TITLE:      Parsing-based View-aware Embedding Network for Vehicle Re-Identification
http://arxiv.org/abs/2004.05021
AUTHORS:        Dechao Meng ; Liang Li ; Xuejing Liu ; Yadong Li ; Shijie Yang ; Zhengjun Zha ; Xingyu Gao ; Shuhui Wang ; Qingming Huang
COMMENTS:       10 pages, 6 figures
HIGHLIGHT:      In this paper, we propose a parsing-based view-aware embedding network (PVEN) to achieve the view-aware feature alignment and enhancement for vehicle ReID.

58, TITLE:      ModuleNet: Knowledge-inherited Neural Architecture Search
http://arxiv.org/abs/2004.05020
AUTHORS:        Yaran Chen ; Ruiyuan Gao ; Fenggang Liu ; Dongbin Zhao
HIGHLIGHT:      In this paper, we discuss what kind of knowledge in a model can and should be used for new architecture design.

59, TITLE:      Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR
http://arxiv.org/abs/2004.05009
AUTHORS:        Hirofumi Inaguma ; Yashesh Gaur ; Liang Lu ; Jinyu Li ; Yifan Gong
COMMENTS:       Accepted at IEEE ICASSP2020
HIGHLIGHT:      Recently, a few novel streaming attention-based sequence-to-sequence (S2S) models have been proposed to perform online speech recognition with linear-time decoding complexity.

60, TITLE:      Bounding the Mim-Width of Hereditary Graph Classes
http://arxiv.org/abs/2004.05018
AUTHORS:        Nick Brettell ; Jake Horsfield ; Andrea Munaro ; Giacomo Paesani ; Daniel Paulusma
HIGHLIGHT:      Combining these with known results, we present summary theorems of the current state of the art for the boundedness of mim-width for $(H_1,H_2)$-free graphs.

61, TITLE:      Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics
http://arxiv.org/abs/2004.05048
AUTHORS:        Shukun Zhang ; James M. Murphy
COMMENTS:       5 pages, 2 columns, 9 figures
HIGHLIGHT:      We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances.

62, TITLE:      Learning to Explore using Active Neural SLAM
http://arxiv.org/abs/2004.05155
AUTHORS:        Devendra Singh Chaplot ; Dhiraj Gandhi ; Saurabh Gupta ; Abhinav Gupta ; Ruslan Salakhutdinov
COMMENTS:       Published in ICLR-2020. See the project webpage at https://devendrachaplot.github.io/projects/Neural-SLAM for supplementary videos. The code is available at https://github.com/devendrachaplot/Neural-SLAM
HIGHLIGHT:      This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called `Active Neural SLAM'.

63, TITLE:      Model Uncertainty Quantification for Reliable Deep Vision Structural Health Monitoring
http://arxiv.org/abs/2004.05151
AUTHORS:        Seyed Omid Sajedi ; Xiao Liang
HIGHLIGHT:      The proposed methodology in this paper can be applied to future deep vision SHM frameworks to incorporate model uncertainty in the inspection processes.

64, TITLE:      Longformer: The Long-Document Transformer
http://arxiv.org/abs/2004.05150
AUTHORS:        Iz Beltagy ; Matthew E. Peters ; Arman Cohan
HIGHLIGHT:      To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer.

65, TITLE:      Early Disease Diagnosis for Rice Crop
http://arxiv.org/abs/2004.04775
AUTHORS:        M. Hammad Masood ; Habiba Saim ; Murtaza Taj ; Mian M. Awais
COMMENTS:       Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)
HIGHLIGHT:      Unlike existing approaches, instead of classifying images into either healthy or diseased, we propose to provide localized classification for each segment of an images. We instead propose a dataset with annotations for each diseased segment in each image.

66, TITLE:      Parameterized Verification of Systems with Global Synchronization and Guards
http://arxiv.org/abs/2004.04896
AUTHORS:        Nouraldin Jaber ; Swen Jacobs ; Christopher Wagner ; Milind Kulkarni ; Roopsha Samanta
COMMENTS:       Accepted at CAV 2020
HIGHLIGHT:      To facilitate the parameterized verification of distributed systems that are based on agreement protocols like consensus and leader election, we define a new computational model for parameterized systems that is based on a general global synchronization primitive and allows for global transition guards.

67, TITLE:      On the Existence of Tacit Assumptions in Contextualized Language Models
http://arxiv.org/abs/2004.04877
AUTHORS:        Nathaniel Weir ; Adam Poliak ; Benjamin Van Durme
COMMENTS:       Pre-Print
HIGHLIGHT:      We find models to be profoundly effective at retrieving concepts given associated properties.

68, TITLE:      D-SRGAN: DEM Super-Resolution with Generative Adversarial Network
http://arxiv.org/abs/2004.04788
AUTHORS:        Bekir Z Demiray ; Muhammed Sit ; Ibrahim Demir
COMMENTS:       8 pages, 8 figures, 2 tables
HIGHLIGHT:      In this paper, a GAN based model is developed and evaluated, inspired by single image super-resolution methods, to increase the spatial resolution of a given DEM dataset up to 4 times without additional information related to data.

69, TITLE:      Using Skill Rating as Fitness on the Evolution of GANs
http://arxiv.org/abs/2004.04796
AUTHORS:        Victor Costa ; Nuno Lourenço ; João Correia ; Penousal Machado
COMMENTS:       To be published in EvoApplications 2020
HIGHLIGHT:      In this work we propose the evaluation of a game-based fitness function to be used within the COEGAN method.

70, TITLE:      Exemplar VAEs for Exemplar based Generation and Data Augmentation
http://arxiv.org/abs/2004.04795
AUTHORS:        Sajad Norouzi ; David J. Fleet ; Mohammad Norouzi
HIGHLIGHT:      This paper presents a framework for exemplar based generative modeling, featuring Exemplar VAEs.

71, TITLE:      Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control
http://arxiv.org/abs/2004.04778
AUTHORS:        Lucas N. Alegre ; Ana L. C. Bazzan ; Bruno C. da Silva
COMMENTS:       13 pages
HIGHLIGHT:      In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent.

72, TITLE:      An End-to-End Learning Approach for Trajectory Prediction in Pedestrian Zones
http://arxiv.org/abs/2004.04787
AUTHORS:        Ha Q. Ngo ; Christoph Henke ; Frank Hees
COMMENTS:       Submitted 23 March 2020
HIGHLIGHT:      This paper aims to explore the problem of trajectory prediction in heterogeneous pedestrian zones, where social dynamics representation is a big challenge.



==========Updates to Previous Papers==========
1, TITLE:       Flexible Transmitter Network
http://arxiv.org/abs/2004.03839
AUTHORS:        Shao-Qun Zhang ; Zhi-Hua Zhou
HIGHLIGHT:      In this paper, we propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron with flexible plasticity.

2, TITLE:       Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation
http://arxiv.org/abs/1912.00320
AUTHORS:        Wen Zhang ; Dongrui Wu
COMMENTS:       Int'l Joint Conf. on Neural Networks (IJCNN), Glasgow, UK, July 2020
HIGHLIGHT:      To address these issues, discriminative joint probability MMD (DJP-MMD) is proposed in this paper to replace the frequently-used joint MMD in domain adaptation.

3, TITLE:       Reactive Probabilistic Programming
http://arxiv.org/abs/1908.07563
AUTHORS:        Guillaume Baudart ; Louis Mandel ; Eric Atkinson ; Benjamin Sherman ; Marc Pouzet ; Michael Carbin
COMMENTS:       Version with appendices of the PLDI 2020 paper "Reactive Probabilistic Programming"
HIGHLIGHT:      In this paper we present ProbZelus the first synchronous probabilistic programming language.

4, TITLE:       Cross-Domain Deep Face Matching for Real Banking Security Systems
http://arxiv.org/abs/1806.07644
AUTHORS:        Johnatan S. Oliveira ; Gustavo B. Souza ; Anderson R. Rocha ; Flávio E. Deus ; Aparecido N. Marana
HIGHLIGHT:      In this work, in addition to collecting a large cross-domain face dataset, with 27,002 real facial images of selfies and ID documents (13,501 subjects) captured from the databases of the major public Brazilian bank, we propose a novel architecture for such cross-domain matching problem based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN).

5, TITLE:       Learning Architectures for Binary Networks
http://arxiv.org/abs/2002.06963
AUTHORS:        Dahyun Kim ; Kunal Pratap Singh ; Jonghyun Choi
COMMENTS:       The manuscript was changed to a one-column format along with minor modifications to the content
HIGHLIGHT:      Questioning that the architectures designed for floating point networks would not be the best for binary networks, we propose to search architectures for binary networks (BNAS) by defining a new search space for binary architectures and a novel search objective.

6, TITLE:       Text Complexity Classification Based on Linguistic Information: Application to Intelligent Tutoring of ESL
http://arxiv.org/abs/2001.01863
AUTHORS:        M. Zakaria Kurdi
COMMENTS:       This is an unpublished pre-print, the JDMDH journal requires submission to arxiv.org before the submission to the journal (see the link: https://jdmdh.episciences.org/page/submissions#)
HIGHLIGHT:      The goal of this work is to build a classifier that can identify text complexity within the context of teaching reading to English as a Second Language (ESL) learners.

7, TITLE:       Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments
http://arxiv.org/abs/1910.11109
AUTHORS:        Zhen-Liang Ni ; Gui-Bin Bian ; Zeng-Guang Hou ; Xiao-Hu Zhou ; Xiao-Liang Xie ; Zhen Li
COMMENTS:       Accepted by ICRA2020; Camera ready
HIGHLIGHT:      In this paper, we propose an attention-guided lightweight network (LWANet), which can segment surgical instruments in real-time.

8, TITLE:       ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
http://arxiv.org/abs/1907.02545
AUTHORS:        Weiwei Sun ; Wei Jiang ; Eduard Trulls ; Andrea Tagliasacchi ; Kwang Moo Yi
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we propose Attentive Context Normalization (ACN), a simple yet effective technique to build permutation-equivariant networks robust to outliers.

9, TITLE:       A Unified MRC Framework for Named Entity Recognition
http://arxiv.org/abs/1910.11476
AUTHORS:        Xiaoya Li ; Jingrong Feng ; Yuxian Meng ; Qinghong Han ; Fei Wu ; Jiwei Li
HIGHLIGHT:      In this paper, we propose a unified framework that is capable of handling both flat and nested NER tasks.

10, TITLE:      Unsupervised Representation Learning with Minimax Distance Measures
http://arxiv.org/abs/1904.13223
AUTHORS:        Morteza Haghir Chehreghani
COMMENTS:       32 pages
HIGHLIGHT:      Thereby, we propose an embedding via first summing up the centered matrices and then performing an eigenvalue decomposition to obtain the relevant features.

11, TITLE:      A Simple Joint Model for Improved Contextual Neural Lemmatization
http://arxiv.org/abs/1904.02306
AUTHORS:        Chaitanya Malaviya ; Shijie Wu ; Ryan Cotterell
COMMENTS:       NAACL 2019
HIGHLIGHT:      We present a simple joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora.

12, TITLE:      Parallel/distributed implementation of cellular training for generative adversarial neural networks
http://arxiv.org/abs/2004.04633
AUTHORS:        Emiliano Perez ; Sergio Nesmachnow ; Jamal Toutouh ; Erik Hemberg ; Una-May O'Reily
COMMENTS:       This article has been accepted for publication in IEEE International Parallel and Distributed Processing Symposium, Parallel and Distributed Combinatorics and Optimization, 2020
HIGHLIGHT:      This article presents a parallel/distributed implementation of a cellular competitive coevolutionary method to train two populations of GANs.

13, TITLE:      STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos
http://arxiv.org/abs/2003.08429
AUTHORS:        Ali Athar ; Sabarinath Mahadevan ; Aljoša Ošep ; Laura Leal-Taixé ; Bastian Leibe
COMMENTS:       28 pages, 6 figures
HIGHLIGHT:      In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos.

14, 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.

15, TITLE:      Graph Embedded Pose Clustering for Anomaly Detection
http://arxiv.org/abs/1912.11850
AUTHORS:        Amir Markovitz ; Gilad Sharir ; Itamar Friedman ; Lihi Zelnik-Manor ; Shai Avidan
COMMENTS:       Code is available at https://github.com/amirmk89/gepc. CVPR 2020
HIGHLIGHT:      We propose a new method for anomaly detection of human actions.

16, TITLE:      Background Matting: The World is Your Green Screen
http://arxiv.org/abs/2004.00626
AUTHORS:        Soumyadip Sengupta ; Vivek Jayaram ; Brian Curless ; Steve Seitz ; Ira Kemelmacher-Shlizerman
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We propose a method for creating a matte -- the per-pixel foreground color and alpha -- of a person by taking photos or videos in an everyday setting with a handheld camera.

17, TITLE:      Exactly Sparse Gaussian Variational Inference with Application to Derivative-Free Batch Nonlinear State Estimation
http://arxiv.org/abs/1911.08333
AUTHORS:        Timothy D. Barfoot ; James R. Forbes ; David Yoon
COMMENTS:       Accepted to the International Journal of Robotics Research (IJRR) on 8 April 2020, # IJR-19-3748; 31 pages, 10 figures
HIGHLIGHT:      We present a Gaussian Variational Inference (GVI) technique that can be applied to large-scale nonlinear batch state estimation problems.

18, TITLE:      Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset
http://arxiv.org/abs/1910.01417
AUTHORS:        Dimitrios Kollias ; Stefanos Zafeiriou
HIGHLIGHT:      This paper presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (OMG-Emotion) dataset.

19, TITLE:      Artificial Intelligence in Glioma Imaging: Challenges and Advances
http://arxiv.org/abs/1911.12886
AUTHORS:        Weina Jin ; Mostafa Fatehi ; Kumar Abhishek ; Mayur Mallya ; Brian Toyota ; Ghassan Hamarneh
COMMENTS:       31 pages, 6 figures. Accepted for publication in the Journal of Neural Engineering
HIGHLIGHT:      We provide a review of recent advances in addressing the above challenges.

20, TITLE:      Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison
http://arxiv.org/abs/2003.03924
AUTHORS:        Tengyang Xie ; Nan Jiang
HIGHLIGHT:      We prove performance guarantees of two algorithms for approximating $Q^\star$ in batch reinforcement learning.

21, TITLE:      Detection and Description of Change in Visual Streams
http://arxiv.org/abs/2003.12633
AUTHORS:        Davis Gilton ; Ruotian Luo ; Rebecca Willett ; Greg Shakhnarovich
HIGHLIGHT:      This paper presents a framework for the analysis of changes in visual streams: ordered sequences of images, possibly separated by significant time gaps.

22, TITLE:      Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
http://arxiv.org/abs/2003.12218
AUTHORS:        Xuan Wang ; Xiangchen Song ; Yingjun Guan ; Bangzheng Li ; Jiawei Han
HIGHLIGHT:      We created this CORD-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020-03-13).

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

24, TITLE:      Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales
http://arxiv.org/abs/1907.11539
AUTHORS:        James Pritts ; Zuzana Kukelova ; Viktor Larsson ; Yaroslava Lochman ; Ondřej Chum
COMMENTS:       arXiv admin note: text overlap with arXiv:1807.06110
HIGHLIGHT:      This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from the image of rigidly-transformed coplanar features.

25, TITLE:      Adaptive Exploration for Unsupervised Person Re-Identification
http://arxiv.org/abs/1907.04194
AUTHORS:        Yuhang Ding ; Hehe Fan ; Mingliang Xu ; Yi Yang
COMMENTS:       ACM Transactions on Multimedia Computing, Communications and Application (TOMCCAP)
HIGHLIGHT:      In this paper, we propose an Adaptive Exploration (AE) method to address the domain-shift problem for re-ID in an unsupervised manner.

26, TITLE:      Visual Reaction: Learning to Play Catch with Your Drone
http://arxiv.org/abs/1912.02155
AUTHORS:        Kuo-Hao Zeng ; Roozbeh Mottaghi ; Luca Weihs ; Ali Farhadi
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      In this paper we address the problem of visual reaction: the task of interacting with dynamic environments where the changes in the environment are not necessarily caused by the agent itself. We propose a new dataset for this task, which includes 30K throws of 20 types of objects in different directions with different forces.

27, TITLE:      Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
http://arxiv.org/abs/1911.11033
AUTHORS:        Mehmet Ozgur Turkoglu ; Stefano D'Aronco ; Jan Dirk Wegner ; Konrad Schindler
HIGHLIGHT:      We propose a new stackable recurrent cell (STAR) for recurrent neural networks (RNNs) that has significantly less parameters than widely used LSTM and GRU while being more robust against vanishing or exploding gradients.

28, TITLE:      Synchronization under Dynamic Constraints
http://arxiv.org/abs/1910.01935
AUTHORS:        Petra Wolf
HIGHLIGHT:      We present three attempts to model constraints of these kinds on the order in which the states of an automaton are transitioned by a synchronizing word.

29, TITLE:      Scene Text Recognition via Transformer
http://arxiv.org/abs/2003.08077
AUTHORS:        Xinjie Feng ; Hongxun Yao ; Yuankai Qi ; Jun Zhang ; Shengping Zhang
HIGHLIGHT:      In this paper, we find that the rectification is completely unnecessary.

30, TITLE:      Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
http://arxiv.org/abs/1911.04910
AUTHORS:        Yun Tang ; Jing Huang ; Guangtao Wang ; Xiaodong He ; Bowen Zhou
HIGHLIGHT:      In this work, we propose a novel translational distance-based approach for knowledge graph link prediction.

31, TITLE:      Slicing and dicing soccer: automatic detection of complex events from spatio-temporal data
http://arxiv.org/abs/2004.04147
AUTHORS:        Lia Morra ; Francesco Manigrasso ; Giuseppe Canto ; Claudio Gianfrate ; Enrico Guarino ; Fabrizio Lamberti
COMMENTS:       accepted at 17th International Conference on Image Analysis and Recognition ICIAR 2020
HIGHLIGHT:      This paper presents a comprehensive approach for de-tecting a wide range of complex events in soccer videos starting frompositional data.

32, TITLE:      Learning to Super Resolve Intensity Images from Events
http://arxiv.org/abs/1912.01196
AUTHORS:        S. Mohammad Mostafavi I. ; Jonghyun Choi ; Kuk-Jin Yoon
COMMENTS:       To appear in CVPR 2020 as an oral presentation
HIGHLIGHT:      We propose an end-to-end network to reconstruct high resolution, high dynamic range (HDR) images directly from the event stream.

33, TITLE:      Dice Loss for Data-imbalanced NLP Tasks
http://arxiv.org/abs/1911.02855
AUTHORS:        Xiaoya Li ; Xiaofei Sun ; Yuxian Meng ; Junjun Liang ; Fei Wu ; Jiwei Li
HIGHLIGHT:      In this paper, we propose to use dice loss in replacement of the standard cross-entropy objective for data-imbalanced NLP tasks.

34, TITLE:      Micro-expression Action Unit Detection with Spatio-temporal Adaptive Pooling
http://arxiv.org/abs/1907.05023
AUTHORS:        Yante Li ; Xiaohua Huang ; Guoying Zhao
COMMENTS:       There is a bug in the method. The results are not correct
HIGHLIGHT:      In this paper, we focus on AU detection in micro-expressions.

35, TITLE:      Multi-Sentence Argument Linking
http://arxiv.org/abs/1911.03766
AUTHORS:        Seth Ebner ; Patrick Xia ; Ryan Culkin ; Kyle Rawlins ; Benjamin Van Durme
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      We present a novel document-level model for finding argument spans that fill an event's roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution.

36, TITLE:      Elephant in the Room: An Evaluation Framework for Assessing Adversarial Examples in NLP
http://arxiv.org/abs/2001.07820
AUTHORS:        Ying Xu ; Xu Zhong ; Antonio Jose Jimeno Yepes ; Jey Han Lau
HIGHLIGHT:      In this paper, we propose an evaluation framework consisting of a set of automatic evaluation metrics and human evaluation guidelines, to rigorously assess the quality of adversarial examples based on the aforementioned properties.

37, TITLE:      Neural Constituency Parsing of Speech Transcripts
http://arxiv.org/abs/1904.08535
AUTHORS:        Paria Jamshid Lou ; Yufei Wang ; Mark Johnson
HIGHLIGHT:      This paper studies the performance of a neural self-attentive parser on transcribed speech.

38, TITLE:      Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach
http://arxiv.org/abs/2003.11163
AUTHORS:        Zhe Zhang ; Chunyu Wang ; Wenhu Qin ; Wenjun Zeng
COMMENTS:       Accepted by CVPR 2020. Code is released at https://github.com/CHUNYUWANG/imu-human-pose-pytorch
HIGHLIGHT:      We present a geometric approach to reinforce the visual features of each pair of joints based on the IMUs.

39, TITLE:      Multi-Objective Matrix Normalization for Fine-grained Visual Recognition
http://arxiv.org/abs/2003.13272
AUTHORS:        Shaobo Min ; Hantao Yao ; Hongtao Xie ; Zheng-Jun Zha ; Yongdong Zhang
HIGHLIGHT:      In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity.

40, TITLE:      Dont Even Look Once: Synthesizing Features for Zero-Shot Detection
http://arxiv.org/abs/1911.07933
AUTHORS:        Pengkai Zhu ; Hanxiao Wang ; Venkatesh Saligrama
COMMENTS:       Accepted at CVPR 2020. 10 pages, 4 figures, 3 tables
HIGHLIGHT:      We propose a novel detection algorithm Dont Even Look Once (DELO), that synthesizes visual features for unseen objects and augments existing training algorithms to incorporate unseen object detection.

41, TITLE:      FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary
http://arxiv.org/abs/1902.03264
AUTHORS:        Yingzhen Yang ; Jiahui Yu ; Nebojsa Jojic ; Jun Huan ; Thomas S. Huang
COMMENTS:       published at ICLR 2020
HIGHLIGHT:      We present a novel method of compression of deep Convolutional Neural Networks (CNNs) by weight sharing through a new representation of convolutional filters.

42, TITLE:      Learning Nonlinear Loop Invariants with Gated Continuous Logic Networks
http://arxiv.org/abs/2003.07959
AUTHORS:        Jianan Yao ; Gabriel Ryan ; Justin Wong ; Suman Jana ; Ronghui Gu
HIGHLIGHT:      In this paper, we introduce a new neural architecture for general SMT learning, the Gated Continuous Logic Network (G-CLN), and apply it to nonlinear loop invariant learning.

43, TITLE:      CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA
http://arxiv.org/abs/2004.04641
AUTHORS:        Alireza Ghaffari ; Yvon Savaria
HIGHLIGHT:      This paper introduces an integrated framework (CNN2Gate) that supports compilation of a CNN model for an FPGA target.

44, TITLE:      Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
http://arxiv.org/abs/2004.04668
AUTHORS:        Neerav Karani ; Krishna Chaitanya ; Ender Konukoglu
COMMENTS:       Pre-print (currently under review at a Journal)
HIGHLIGHT:      We employ an independently trained denoising autoencoder (DAE) in order to model such an implicit prior on plausible anatomical segmentation labels.

45, TITLE:      Image Segmentation Using Deep Learning: A Survey
http://arxiv.org/abs/2001.05566
AUTHORS:        Shervin Minaee ; Yuri Boykov ; Fatih Porikli ; Antonio Plaza ; Nasser Kehtarnavaz ; Demetri Terzopoulos
HIGHLIGHT:      In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings.

46, TITLE:      U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation
http://arxiv.org/abs/2004.03466
AUTHORS:        Shuhang Wang ; Szu-Yeu Hu ; Eugene Cheah ; Xiaohong Wang ; Jingchao Wang ; Lei Chen ; Masoud Baikpour ; Arinc Ozturk ; Qian Li ; Shinn-Huey Chou ; Constance D. Lehman ; Viksit Kumar ; Anthony Samir
COMMENTS:       8 pages MICCAI
HIGHLIGHT:      This paper proposes a novel U-Net variant using stacked dilated convolutions for medical image segmentation (SDU-Net).

47, TITLE:      Distributed Soft Actor-Critic with Multivariate Reward Representation and Knowledge Distillation
http://arxiv.org/abs/1911.13056
AUTHORS:        Dmitry Akimov
COMMENTS:       9 pages, 5 figures
HIGHLIGHT:      In this paper, we describe NeurIPS 2019 Learning to Move - Walk Around challenge physics-based environment and present our solution to this competition which scored 1303.727 mean reward points and took 3rd place.

48, TITLE:      Disfluency Detection using Auto-Correlational Neural Networks
http://arxiv.org/abs/1808.09092
AUTHORS:        Paria Jamshid Lou ; Peter Anderson ; Mark Johnson
HIGHLIGHT:      As an alternative, this paper proposes a simple yet effective model for automatic disfluency detection, called an auto-correlational neural network (ACNN).

49, TITLE:      Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning
http://arxiv.org/abs/2003.13261
AUTHORS:        Shaobo Min ; Hantao Yao ; Hongtao Xie ; Chaoqun Wang ; Zheng-Jun Zha ; Yongdong Zhang
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this paper, we propose a novel Domain-aware Visual Bias Eliminating (DVBE) network that constructs two complementary visual representations, i.e., semantic-free and semantic-aligned, to treat seen and unseen domains separately.

50, TITLE:      A multimodal deep learning approach for named entity recognition from social media
http://arxiv.org/abs/2001.06888
AUTHORS:        Meysam Asgari-Chenaghlu ; M. Reza Feizi-Derakhshi ; Leili Farzinvash ; M. A. Balafar ; Cina Motamed
HIGHLIGHT:      We propose two novel deep learning approaches utilizing multimodal deep learning and Transformers.

51, TITLE:      XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization
http://arxiv.org/abs/2003.11080
AUTHORS:        Junjie Hu ; Sebastian Ruder ; Aditya Siddhant ; Graham Neubig ; Orhan Firat ; Melvin Johnson
HIGHLIGHT:      To this end, we introduce the Cross-lingual TRansfer Evaluation of Multilingual Encoders XTREME benchmark, a multi-task benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We release the benchmark to encourage research on cross-lingual learning methods that transfer linguistic knowledge across a diverse and representative set of languages and tasks.

52, TITLE:      Very high resolution Airborne PolSAR Image Classification using Convolutional Neural Networks
http://arxiv.org/abs/1910.14578
AUTHORS:        Minh-Tan Pham ; Sébastien Lefèvre
COMMENTS:       5 pages, accepted in EUSAR 2020
HIGHLIGHT:      In this work, we exploit convolutional neural networks (CNNs) for the classification of very high resolution (VHR) polarimetric SAR (PolSAR) data.

53, TITLE:      Joint Visual Grounding with Language Scene Graphs
http://arxiv.org/abs/1906.03561
AUTHORS:        Daqing Liu ; Hanwang Zhang ; Zheng-Jun Zha ; Meng Wang ; Qianru Sun
HIGHLIGHT:      In this paper, we alleviate the missing-annotation problem and enable the joint reasoning by leveraging the language scene graph which covers both labeled referent and unlabeled contexts (other objects, attributes, and relationships).

54, TITLE:      Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
http://arxiv.org/abs/2004.03386
AUTHORS:        Su Zhu ; Jieyu Li ; Lu Chen ; Kai Yu
COMMENTS:       13 pages, 3 figures
HIGHLIGHT:      In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms.

55, TITLE:      DeepCOVIDExplainer: Explainable COVID-19 Predictions Based on Chest X-ray Images
http://arxiv.org/abs/2004.04582
AUTHORS:        Md. Rezaul Karim ; Till Döhmen ; Dietrich Rebholz-Schuhmann ; Stefan Decker ; Michael Cochez ; Oya Beyan
HIGHLIGHT:      In this paper, we propose an explainable deep neural networks(DNN)-based method for automatic detection of COVID-19 symptoms from CXR images, which we call 'DeepCOVIDExplainer'.

56, TITLE:      DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation
http://arxiv.org/abs/2004.02164
AUTHORS:        Xuefei Ning ; Tianchen Zhao ; Wenshuo Li ; Peng Lei ; Yu Wang ; Huazhong Yang
COMMENTS:       The first two authors contribute equally
HIGHLIGHT:      In this paper, we propose Differentiable Sparsity Allocation (DSA), an efficient end-to-end budgeted pruning flow.

57, TITLE:      CineFilter: Unsupervised Filtering for Real Time Autonomous Camera Systems
http://arxiv.org/abs/1912.05636
AUTHORS:        Sudheer Achary ; Syed Ashar Javed ; Nikita Shravan ; K L Bhanu Moorthy ; Vineet Gandhi ; Anoop Namboodiri
HIGHLIGHT:      We propose two models, one a convex optimization based approach and another a CNN based model, both of which can exploit the temporal trends in the camera behavior.