2020.04.02.txt

==========New Papers==========
1, TITLE:       Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication
http://arxiv.org/abs/2004.00470
AUTHORS:        Jianyu Su ; Stephen Adams ; Peter A. Beling
COMMENTS:       Submitted to ECML
HIGHLIGHT:      In this study, we develop an architecture that allows for communication among agents and tailors the system's reward for each individual agent.

2, TITLE:       A New Challenge: Approaching Tetris Link with AI
http://arxiv.org/abs/2004.00377
AUTHORS:        Matthias Muller-Brockhausen ; Mike Preuss ; Aske Plaat
HIGHLIGHT:      We explore heuristic planning and two other approaches: Reinforcement Learning, Monte Carlo tree search.

3, TITLE:       Graph Structured Network for Image-Text Matching
http://arxiv.org/abs/2004.00277
AUTHORS:        Chunxiao Liu ; Zhendong Mao ; Tianzhu Zhang ; Hongtao Xie ; Bin Wang ; Yongdong Zhang
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this paper, we present a novel Graph Structured Matching Network (GSMN) to learn fine-grained correspondence.

4, TITLE:       Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing
http://arxiv.org/abs/2004.00280
AUTHORS:        Hengtong Hu ; Lingxi Xie ; Richang Hong ; Qi Tian
COMMENTS:       This paper has been accepted for CVPR2020
HIGHLIGHT:      In this paper, we propose a novel approach that enables guiding a supervised method using outputs produced by an unsupervised method.

5, TITLE:       Progressive Multi-Stage Learning for Discriminative Tracking
http://arxiv.org/abs/2004.00255
AUTHORS:        Weichao Li ; Xi Li ; Omar Elfarouk Bourahla ; Fuxian Huang ; Fei Wu ; Wei Liu ; Zhiheng Wang ; Hongmin Liu
COMMENTS:       accepted to IEEE Transactions on Cybernetics
HIGHLIGHT:      To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multi-stage optimization policy of sample selection for robust visual tracking.

6, TITLE:       An Efficient Agreement Mechanism in CapsNets By Pairwise Product
http://arxiv.org/abs/2004.00272
AUTHORS:        Lei Zhao ; Xiaohui Wang ; Lei Huang
COMMENTS:       Accepted to ECAI 2020
HIGHLIGHT:      This paper proposes a pairwise agreement mechanism to build capsules, inspired by the feature interactions of factorization machines (FMs).

7, TITLE:       Mimicking Evolution with Reinforcement Learning
http://arxiv.org/abs/2004.00048
AUTHORS:        João P. Abrantes ; Arnaldo J. Abrantes ; Frans A. Oliehoek
COMMENTS:       18 pages, 7 figures
HIGHLIGHT:      This work proposes Evolution via Evolutionary Reward (EvER) that allows learning to single-handedly drive the search for policies with increasingly evolutionary fitness by ensuring the alignment of the reward function with the fitness function.

8, TITLE:       Unification-based Reconstruction of Explanations for Science Questions
http://arxiv.org/abs/2004.00061
AUTHORS:        Marco Valentino ; Mokanarangan Thayaparan ; André Freitas
HIGHLIGHT:      The paper presents a framework to reconstruct explanations for multiple choices science questions through explanation-centred corpora.

9, TITLE:       Sign Language Translation with Transformers
http://arxiv.org/abs/2004.00588
AUTHORS:        Kayo Yin
COMMENTS:       14 pages, 6 figures
HIGHLIGHT:      We report a wide range of experimental results for various Transformer setups and introduce the use of Spatial-Temporal Multi-Cue (STMC) networks in an end-to-end SLT system with Transformer.

10, TITLE:      An Improved Classification Model for Igbo Text Using N-Gram And K-Nearest Neighbour Approaches
http://arxiv.org/abs/2004.00375
AUTHORS:        Nkechi Ifeanyi-Reuben ; Chidiebere Ugwu
HIGHLIGHT:      This paper presents an improved classification model for Igbo text using N-gram and K-Nearest Neighbour approaches.

11, TITLE:      Personal Health Knowledge Graphs for Patients
http://arxiv.org/abs/2004.00071
AUTHORS:        Nidhi Rastogi ; Mohammed J. Zaki
COMMENTS:       3 pages, workshop paper
HIGHLIGHT:      Personal Health Knowledge Graphs for Patients

12, TITLE:      High-Performance Long-Term Tracking with Meta-Updater
http://arxiv.org/abs/2004.00305
AUTHORS:        Kenan Dai ; Yunhua Zhang ; Dong Wang ; Jianhua Li ; Huchuan Lu ; Xiaoyun Yang
HIGHLIGHT:      In this work, we propose a novel offline-trained Meta-Updater to address an important but unsolved problem: Is the tracker ready for updating in the current frame?

13, TITLE:      CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
http://arxiv.org/abs/2004.00288
AUTHORS:        Yuge Huang ; Yuhan Wang ; Ying Tai ; Xiaoming Liu ; Pengcheng Shen ; Shaoxin Li ; Jilin Li ; Feiyue Huang
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage.

14, TITLE:      Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
http://arxiv.org/abs/2004.00306
AUTHORS:        Sravanti Addepalli ; Vivek B. S. ; Arya Baburaj ; Gaurang Sriramanan ; R. Venkatesh Babu
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we attempt to address this problem by training networks to form coarse impressions based on the information in higher bit planes, and use the lower bit planes only to refine their prediction.

15, TITLE:      Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage
http://arxiv.org/abs/2004.00292
AUTHORS:        Christoffer Bøgelund Rasmussen ; Thomas B. Moeslund
COMMENTS:       Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)
HIGHLIGHT:      Therefore, we investigate a number of state of the art CNN models for the task of measuring kernel fragmentation in harvested corn silage.

16, TITLE:      SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans
http://arxiv.org/abs/2004.00326
AUTHORS:        Igor Santesteban ; Elena Garces ; Miguel A. Otaduy ; Dan Casas
COMMENTS:       Accepted at Eurographics 2020. Project website: http://dancasas.github.io/projects/SoftSMPL
HIGHLIGHT:      We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion.

17, TITLE:      Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation
http://arxiv.org/abs/2004.00329
AUTHORS:        Matteo Fabbri ; Fabio Lanzi ; Simone Calderara ; Stefano Alletto ; Rita Cucchiara
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper we present a novel approach for bottom-up multi-person 3D human pose estimation from monocular RGB images.

18, TITLE:      Learning to Select Base Classes for Few-shot Classification
http://arxiv.org/abs/2004.00315
AUTHORS:        Linjun Zhou ; Peng Cui ; Xu Jia ; Shiqiang Yang ; Qi Tian
HIGHLIGHT:      In this paper, we utilize a simple yet effective measure, the Similarity Ratio, as an indicator for the generalization performance of a few-shot model.

19, TITLE:      Transfer Learning of Photometric Phenotypes in Agriculture Using Metadata
http://arxiv.org/abs/2004.00303
AUTHORS:        Dan Halbersberg ; Aharon Bar Hillel ; Shon Mendelson ; Daniel Koster ; Lena Karol ; Boaz Lerner
COMMENTS:       Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)
HIGHLIGHT:      We combine the image and metadata regarding capturing conditions embedded into a network, enabling more accurate estimation and transfer between different conditions.

20, TITLE:      More Grounded Image Captioning by Distilling Image-Text Matching Model
http://arxiv.org/abs/2004.00390
AUTHORS:        Yuanen Zhou ; Meng Wang ; Daqing Liu ; Zhenzhen Hu ; Hanwang Zhang
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      To this end, we propose a Part-of-Speech (POS) enhanced image-text matching model (SCAN \cite{lee2018stacked}): POS-SCAN, as the effective knowledge distillation for more grounded image captioning.

21, TITLE:      Single Image Optical Flow Estimation with an Event Camera
http://arxiv.org/abs/2004.00347
AUTHORS:        Liyuan Pan ; Miaomiao Liu ; Richard Hartley
COMMENTS:       CVPR2020
HIGHLIGHT:      In this paper, we propose a single image (potentially blurred) and events based optical flow estimation approach.

22, TITLE:      Two-shot Spatially-varying BRDF and Shape Estimation
http://arxiv.org/abs/2004.00403
AUTHORS:        Mark Boss ; Varun Jampani ; Kihwan Kim ; Hendrik P. A. Lensch ; Jan Kautz
HIGHLIGHT:      We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF. We also create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials.

23, TITLE:      Image Demoireing with Learnable Bandpass Filters
http://arxiv.org/abs/2004.00406
AUTHORS:        Bolun Zheng ; Shanxin Yuan ; Gregory Slabaugh ; Ales Leonardis
COMMENTS:       Accepted by CVPR2020. Code is available at https://github.com/zhenngbolun/Learnbale_Bandpass_Filter
HIGHLIGHT:      In this paper, we propose a novel multiscale bandpass convolutional neural network (MBCNN) to address this problem.

24, TITLE:      M2m: Imbalanced Classification via Major-to-minor Translation
http://arxiv.org/abs/2004.00431
AUTHORS:        Jaehyung Kim ; Jongheon Jeong ; Jinwoo Shin
COMMENTS:       12 pages; Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples (e.g., images) from more-frequent classes.

25, TITLE:      Digit Recognition Using Convolution Neural Network
http://arxiv.org/abs/2004.00331
AUTHORS:        Kajol Gupta
COMMENTS:       7 pages, 4 figures and 2 tables
HIGHLIGHT:      The main objective of this work is to obtain highest accuracy 99.15% by using convolution neural network (CNN) to recognize the digit without doing too much pre-processing of dataset.

26, TITLE:      Semantic Drift Compensation for Class-Incremental Learning
http://arxiv.org/abs/2004.00440
AUTHORS:        Lu Yu ; Bartłomiej Twardowski ; Xialei Liu ; Luis Herranz ; Kai Wang ; Yongmei Cheng ; Shangling Jui ; Joost van de Weijer
COMMENTS:       Accepted at CVPR2020, Code available at \url{https://github.com/yulu0724/SDC-IL}
HIGHLIGHT:      Therefore, we study incremental learning for embedding networks.

27, TITLE:      Spatio-temporal Tubelet Feature Aggregation and Object Linking in Videos
http://arxiv.org/abs/2004.00451
AUTHORS:        Daniel Cores ; Víctor M. Brea ; Manuel Mucientes
HIGHLIGHT:      We propose a two stage object detector called FANet based on short-term spatio-temporal feature aggregation to give a first detection set, and long-term object linking to refine these detections. Firstly, we generate a set of short tubelet proposals containing the object in $N$ consecutive frames.

28, TITLE:      PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
http://arxiv.org/abs/2004.00452
AUTHORS:        Shunsuke Saito ; Tomas Simon ; Jason Saragih ; Hanbyul Joo
COMMENTS:       project page: https://shunsukesaito.github.io/PIFuHD
HIGHLIGHT:      We argue that this limitation stems primarily form two conflicting requirements; accurate predictions require large context, but precise predictions require high resolution.

29, TITLE:      Medical-based Deep Curriculum Learning for Improved Fracture Classification
http://arxiv.org/abs/2004.00482
AUTHORS:        Amelia Jiménez-Sánchez ; Diana Mateus ; Sonja Kirchhoff ; Chlodwig Kirchhoff ; Peter Biberthaler ; Nassir Navab ; Miguel A. González Ballester ; Gemma Piella
COMMENTS:       MICCAI 2019
HIGHLIGHT:      In this work, we propose and compare several strategies relying on curriculum learning, to support the classification of proximal femur fracture from X-ray images, a challenging problem as reflected by existing intra- and inter-expert disagreement.

30, TITLE:      Learning to Cluster Faces via Confidence and Connectivity Estimation
http://arxiv.org/abs/2004.00445
AUTHORS:        Lei Yang ; Dapeng Chen ; Xiaohang Zhan ; Rui Zhao ; Chen Change Loy ; Dahua Lin
COMMENTS:       8 pages, 6 figures, CVPR 2020
HIGHLIGHT:      In this paper, we propose a fully learnable clustering framework without requiring a large number of overlapped subgraphs.

31, TITLE:      Boosting Deep Hyperspectral Image Classification with Spectral Unmixing
http://arxiv.org/abs/2004.00583
AUTHORS:        Alan J. X. Guo ; Fei Zhu
HIGHLIGHT:      To tackle the overfitting issue, we propose an abundance-based multi-HSI classification method.

32, TITLE:      Symmetry and Group in Attribute-Object Compositions
http://arxiv.org/abs/2004.00587
AUTHORS:        Yong-Lu Li ; Yue Xu ; Xiaohan Mao ; Cewu Lu
COMMENTS:       Accepted to CVPR 2020, supplementary materials included, code available:https://github.com/DirtyHarryLYL/SymNet
HIGHLIGHT:      Incorporating the symmetry principle, a transformation framework inspired by group theory is built, i.e. SymNet.

33, TITLE:      EPOS: Estimating 6D Pose of Objects with Symmetries
http://arxiv.org/abs/2004.00605
AUTHORS:        Tomas Hodan ; Daniel Barath ; Jiri Matas
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image.

34, TITLE:      Evading Deepfake-Image Detectors with White- and Black-Box Attacks
http://arxiv.org/abs/2004.00622
AUTHORS:        Nicholas Carlini ; Hany Farid
HIGHLIGHT:      We show that such forensic classifiers are vulnerable to a range of attacks that reduce the classifier to near-0% accuracy.

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

36, TITLE:      No-regret learning dynamics for extensive-form correlated and coarse correlated equilibria
http://arxiv.org/abs/2004.00603
AUTHORS:        Andrea Celli ; Alberto Marchesi ; Gabriele Farina ; Nicola Gatti
HIGHLIGHT:      In this paper, we show how to leverage the popular counterfactual regret minimization (CFR) paradigm to induce simple no-regret dynamics that converge to the set of EFCEs and EFCCEs in an n-player general-sum extensive-form games.

37, TITLE:      Give your Text Representation Models some Love: the Case for Basque
http://arxiv.org/abs/2004.00033
AUTHORS:        Rodrigo Agerri ; Iñaki San Vicente ; Jon Ander Campos ; Ander Barrena ; Xabier Saralegi ; Aitor Soroa ; Eneko Agirre
COMMENTS:       Accepted at LREC 2020; 8 pages, 7 tables
HIGHLIGHT:      In this paper we show that a number of monolingual models (FastText word embeddings, FLAIR and BERT language models) trained with larger Basque corpora produce much better results than publicly available versions in downstream NLP tasks, including topic classification, sentiment classification, PoS tagging and NER.

38, TITLE:      Multilingual Stance Detection: The Catalonia Independence Corpus
http://arxiv.org/abs/2004.00050
AUTHORS:        Elena Zotova ; Rodrigo Agerri ; Manuel Nuñez ; German Rigau
COMMENTS:       Accepted at LREC 2020; 8 pages 10 tables
HIGHLIGHT:      This paper addresses these issues by presenting a new multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages, with the aim of facilitating research on stance detection in multilingual and cross-lingual settings.

39, TITLE:      A Clustering Framework for Lexical Normalization of Roman Urdu
http://arxiv.org/abs/2004.00088
AUTHORS:        Abdul Rafae Khan ; Asim Karim ; Hassan Sajjad ; Faisal Kamiran ; Jia Xu
HIGHLIGHT:      In this article, we present a feature-based clustering framework for the lexical normalization of Roman Urdu corpora, which includes a phonetic algorithm UrduPhone, a string matching component, a feature-based similarity function, and a clustering algorithm Lex-Var.

40, TITLE:      Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study
http://arxiv.org/abs/2004.00068
AUTHORS:        Michael Leventhal ; Allahsera Tapo ; Sarah Luger ; Marcos Zampieri ; Christopher M. Homan
HIGHLIGHT:      We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages.

41, TITLE:      Adversarial Transfer Learning for Punctuation Restoration
http://arxiv.org/abs/2004.00248
AUTHORS:        Jiangyan Yi ; Jianhua Tao ; Ye Bai ; Zhengkun Tian ; Cunhang Fan
HIGHLIGHT:      This paper proposes adversarial transfer learning to address these problems.

42, TITLE:      A Swiss German Dictionary: Variation in Speech and Writing
http://arxiv.org/abs/2004.00139
AUTHORS:        Larissa Schmidt ; Lucy Linder ; Sandra Djambazovska ; Alexandros Lazaridis ; Tanja Samardžić ; Claudiu Musat
COMMENTS:       6 pages, 1 figure, 2 tables. To be published in: Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020). Marseille, France. For project reports and to obtain the dictionary see http://tiny.uzh.ch/11X
HIGHLIGHT:      We introduce a dictionary containing forms of common words in various Swiss German dialects normalized into High German.

43, TITLE:      Automatic Extraction of Bengali Root Verbs using Paninian Grammar
http://arxiv.org/abs/2004.00089
AUTHORS:        Arijit Das ; Tapas Halder ; Diganta Saha
COMMENTS:       published in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, 2017
HIGHLIGHT:      In this research work, we have proposed an algorithm based on supervised learning methodology to extract the root forms of the Bengali verbs using the grammatical rules proposed by Panini [1] in Ashtadhyayi.

44, TITLE:      Enriching Consumer Health Vocabulary Using Enhanced GloVe Word Embedding
http://arxiv.org/abs/2004.00150
AUTHORS:        Mohammed Ibrahim ; Susan Gauch ; Omar Salman ; Mohammed Alqahatani
HIGHLIGHT:      In this paper, we present an enhanced word embedding technique that generates new CHV terms from a consumer-generated text.

45, TITLE:      LGVTON: A Landmark Guided Approach to Virtual Try-On
http://arxiv.org/abs/2004.00562
AUTHORS:        Debapriya Roy ; Sanchayan Santra ; Bhabatosh Chanda
COMMENTS:       Under Review
HIGHLIGHT:      Keeping this in mind we propose \textit{LGVTON}, a novel self-supervised landmark guided approach to image based virtual try-on.

46, TITLE:      Physically Realizable Adversarial Examples for LiDAR Object Detection
http://arxiv.org/abs/2004.00543
AUTHORS:        James Tu ; Mengye Ren ; Siva Manivasagam ; Ming Liang ; Bin Yang ; Richard Du ; Frank Cheng ; Raquel Urtasun
HIGHLIGHT:      In this paper, we address this issue and present a method to generate universal 3D adversarial objects to fool LiDAR detectors.

47, TITLE:      Feature-Driven Super-Resolution for Object Detection
http://arxiv.org/abs/2004.00554
AUTHORS:        Bin Wang ; Tao Lu ; Yanduo Zhang
COMMENTS:       4 pages, 3 figures
HIGHLIGHT:      This paper proposes a simple but powerful feature-driven super-resolution (FDSR) to improve the detection performance of low-resolution (LR) images.

48, 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.

49, TITLE:      Pose-guided Visible Part Matching for Occluded Person ReID
http://arxiv.org/abs/2004.00230
AUTHORS:        Shang Gao ; Jingya Wang ; Huchuan Lu ; Zimo Liu
COMMENTS:       Accepted to CVPR2020
HIGHLIGHT:      To address this issue, we propose a Pose-guided Visible Part Matching (PVPM) method that jointly learns the discriminative features with pose-guided attention and self-mines the part visibility in an end-to-end framework.

50, TITLE:      Spatio-Temporal Action Detection with Multi-Object Interaction
http://arxiv.org/abs/2004.00180
AUTHORS:        Huijuan Xu ; Lizhi Yang ; Stan Sclaroff ; Kate Saenko ; Trevor Darrell
HIGHLIGHT:      In this paper, we study the spatio-temporal action detection problem with multi-object interaction. We introduce a new dataset that is annotated with action tubes containing multi-object interactions.

51, TITLE:      Knowledge as Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge
http://arxiv.org/abs/2004.00176
AUTHORS:        Long Zhao ; Xi Peng ; Yuxiao Chen ; Mubbasir Kapadia ; Dimitris N. Metaxas
COMMENTS:       In CVPR 2020. (15 pages including supplementary material)
HIGHLIGHT:      In this paper, we propose a novel scheme to train the Student in a Target dataset where the Teacher is unavailable.

52, TITLE:      Shared Cross-Modal Trajectory Prediction for Autonomous Driving
http://arxiv.org/abs/2004.00202
AUTHORS:        Chiho Choi
HIGHLIGHT:      We propose a framework for predicting future trajectories of traffic agents in highly interactive environments.

53, TITLE:      BCNet: Learning Body and Cloth Shape from A Single Image
http://arxiv.org/abs/2004.00214
AUTHORS:        Boyi Jiang ; Juyong Zhang ; Yang Hong ; Jinhao Luo ; Ligang Liu ; Hujun Bao
HIGHLIGHT:      In this paper, we consider the problem to automatically reconstruct both garment and body shapes from a single near front view RGB image. To train our model, we construct two large scale datasets with ground truth body and garment geometries as well as paired color images.

54, TITLE:      The Edge of Depth: Explicit Constraints between Segmentation and Depth
http://arxiv.org/abs/2004.00171
AUTHORS:        Shengjie Zhu ; Garrick Brazil ; Xiaoming Liu
HIGHLIGHT:      In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images.

55, TITLE:      Video Anomaly Detection for Smart Surveillance
http://arxiv.org/abs/2004.00222
AUTHORS:        Sijie Zhu ; Chen Chen ; Waqas Sultani
HIGHLIGHT:      In this paper, we provide a brief overview of the recent research progress on video anomaly detection and highlight a few future research directions.

56, TITLE:      NBDT: Neural-Backed Decision Trees
http://arxiv.org/abs/2004.00221
AUTHORS:        Alvin Wan ; Lisa Dunlap ; Daniel Ho ; Jihan Yin ; Scott Lee ; Henry Jin ; Suzanne Petryk ; Sarah Adel Bargal ; Joseph E. Gonzalez
COMMENTS:       14 pages, 9 figures
HIGHLIGHT:      NBDT: Neural-Backed Decision Trees

57, TITLE:      Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation
http://arxiv.org/abs/2004.00161
AUTHORS:        Victor Schmidt ; Makesh Narsimhan Sreedhar ; Mostafa ElAraby ; Irina Rish
HIGHLIGHT:      To alleviate this, we introduce Lifelong Self-Supervision (LiSS) as a way to pre-train an I2IT model (e.g., CycleGAN) on a set of self-supervised auxiliary tasks.

58, TITLE:      Learning Generative Models of Tissue Organization with Supervised GANs
http://arxiv.org/abs/2004.00140
AUTHORS:        Ligong Han ; Robert F. Murphy ; Deva Ramanan
COMMENTS:       Accepted at WACV-18
HIGHLIGHT:      In this paper, we focus on building generative models of electron microscope (EM) images in which the positions of cell membranes and mitochondria have been densely annotated, and propose a two-stage procedure that produces realistic images using Generative Adversarial Networks (or GANs) in a supervised way.

59, TITLE:      Deep Semantic Matching with Foreground Detection and Cycle-Consistency
http://arxiv.org/abs/2004.00144
AUTHORS:        Yun-Chun Chen ; Po-Hsiang Huang ; Li-Yu Yu ; Jia-Bin Huang ; Ming-Hsuan Yang ; Yen-Yu Lin
COMMENTS:       ACCV 2018. PAMI 2020 extension: arXiv:1906.05857
HIGHLIGHT:      In this paper, we address weakly supervised semantic matching based on a deep network where only image pairs without manual keypoint correspondence annotations are provided.

60, TITLE:      Revisiting Few-shot Activity Detection with Class Similarity Control
http://arxiv.org/abs/2004.00137
AUTHORS:        Huijuan Xu ; Ximeng Sun ; Eric Tzeng ; Abir Das ; Kate Saenko ; Trevor Darrell
HIGHLIGHT:      In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection based on proposal regression which detects the start and end time of the activities in untrimmed videos.

61, TITLE:      StyleRig: Rigging StyleGAN for 3D Control over Portrait Images
http://arxiv.org/abs/2004.00121
AUTHORS:        Ayush Tewari ; Mohamed Elgharib ; Gaurav Bharaj ; Florian Bernard ; Hans-Peter Seidel ; Patrick Pérez ; Michael Zollhöfer ; Christian Theobalt
COMMENTS:       CVPR 2020 (Oral). Project page: https://gvv.mpi-inf.mpg.de/projects/StyleRig/
HIGHLIGHT:      We present the first method to provide a face rig-like control over a pretrained and fixed StyleGAN via a 3DMM.

62, TITLE:      Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning
http://arxiv.org/abs/2004.00567
AUTHORS:        Marco Pleines ; Jenia Jitsev ; Mike Preuss ; Frank Zimmer
COMMENTS:       8 pages, 9 figures, 2 tables, under review
HIGHLIGHT:      Whereas the top 6 performing entries of last year's competition all used human demonstrations to learn how to cope with the challenge, we present an approach that performed competitively (placed 7th) but starts completely from scratch by means of Deep Reinforcement Learning with a relatively simple feed-forward deep network structure.

63, TITLE:      Self-adaptation in non-Elitist Evolutionary Algorithms on Discrete Problems with Unknown Structure
http://arxiv.org/abs/2004.00327
AUTHORS:        Brendan Case ; Per Kristian Lehre
COMMENTS:       To appear in IEEE Transactions of Evolutionary Computation
HIGHLIGHT:      Here we show through a theoretical runtime analysis that a non-elitist, discrete evolutionary algorithm which self-adapts its mutation rate not only outperforms EAs which use static mutation rates on \leadingones, but also improves asymptotically on an EA using a state-of-the-art control mechanism.

64, TITLE:      Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution
http://arxiv.org/abs/2004.00307
AUTHORS:        Filipe Assunção ; Nuno Lourenço ; Bernardete Ribeiro ; Penousal Machado
COMMENTS:       EvoApps 2020
HIGHLIGHT:      In particular, this paper describes AutoML-DSGE - a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines.

65, TITLE:      Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks
http://arxiv.org/abs/2004.00151
AUTHORS:        Jacob Schrum ; Jake Gutierrez ; Vanessa Volz ; Jialin Liu ; Simon Lucas ; Sebastian Risi
COMMENTS:       GECCO 2020
HIGHLIGHT:      Therefore, this paper introduces a tool for interactive LVE of tile-based levels for games.

66, TITLE:      Incremental Evolution and Development of Deep Artificial Neural Networks
http://arxiv.org/abs/2004.00302
AUTHORS:        Filipe Assunção ; Nuno Lourenço ; Bernardete Ribeiro ; Penousal Machado
COMMENTS:       European Conference on Genetic Programming (EuroGP) 2020
HIGHLIGHT:      To overcome this drawback, we extend Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) to incremental development.

67, TITLE:      Particle Swarm Optimization: Stability Analysis using N-Informers under Arbitrary Coefficient Distributions
http://arxiv.org/abs/2004.00476
AUTHORS:        Christopher W Cleghorn ; Belinda Stapelberg
HIGHLIGHT:      This paper derives, under minimal modelling assumptions, a simple to use theorem for obtaining both order-$1$ and order-$2$ stability criteria for a common class of particle swarm optimization (PSO) variants.

68, TITLE:      Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound
http://arxiv.org/abs/2004.00207
AUTHORS:        Chaoyu Chen ; Xin Yang ; Ruobing Huang ; Wenlong Shi ; Shengfeng Liu ; Mingrong Lin ; Yuhao Huang ; Yong Yang ; Yuanji Zhang ; Huanjia Luo ; Yankai Huang ; Yi Xiong ; Dong Ni
COMMENTS:       IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2020)
HIGHLIGHT:      In this work, we exploit an object detection framework to detect landmarks in 3D fetal facial US volumes.

69, TITLE:      Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network
http://arxiv.org/abs/2004.00191
AUTHORS:        Yanglan Ou ; Yuan Xue ; Ye Yuan ; Tao Xu ; Vincent Pisztora ; Jia Li ; Xiaolei Huang
COMMENTS:       ISBI 2020
HIGHLIGHT:      In this paper, we propose a novel and more flexible GCN model with a feature encoder that adaptively updates the adjacency matrix during learning and demonstrate that this model design leads to improved performance.

70, TITLE:      In-Domain GAN Inversion for Real Image Editing
http://arxiv.org/abs/2004.00049
AUTHORS:        Jiapeng Zhu ; Yujun Shen ; Deli Zhao ; Bolei Zhou
COMMENTS:       29 pages, 20 figures, 2 tables
HIGHLIGHT:      To solve this problem, we propose an in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing.

71, TITLE:      You can do RLAs for IRV
http://arxiv.org/abs/2004.00235
AUTHORS:        Michelle Blom ; Andrew Conway ; Dan King ; Laurent Sandrolini ; Philip B. Stark ; Peter J. Stuckey ; Vanessa Teague
HIGHLIGHT:      This report describes the first ever process pilot of Risk Limiting Audits for IRV, for the San Francisco District Attorney's race in November, 2019.

72, TITLE:      Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning
http://arxiv.org/abs/2004.00251
AUTHORS:        Jin-Woo Seo ; Hong-Gyu Jung ; Seong-Whan Lee
COMMENTS:       The first two authors contributed equally
HIGHLIGHT:      To tackle this issue, we propose self-augmentation that consolidates regional dropout and self-distillation.

73, TITLE:      A macro agent and its actions
http://arxiv.org/abs/2004.00058
AUTHORS:        Larissa Albantakis ; Francesco Massari ; Maggie Beheler-Amass ; Giulio Tononi
COMMENTS:       18 pages, 5 figures; to appear as a chapter in "Top-Down Causation and Emergence" published by Springer as part of the Synthese Library Book Series; F.M. and M.B. contributed equally to this work
HIGHLIGHT:      Here, we demonstrate this framework by example of a simulated agent, equipped with a small neural network, that forms a maximum of $\Phi$ at a macro scale.

74, TITLE:      Explosive Proofs of Mathematical Truths
http://arxiv.org/abs/2004.00055
AUTHORS:        Scott Viteri ; Simon DeDeo
COMMENTS:       16 pages, 5 figures. Comments solicited
HIGHLIGHT:      Here we show that under a cognitively-plausible belief formation mechanism that combines deductive and abductive reasoning, mathematical arguments can undergo what we call an epistemic phase transition: a dramatic and rapidly-propagating jump from uncertainty to near-complete confidence at reasonable levels of claim-to-claim error rates.

75, TITLE:      AM-MobileNet1D: A Portable Model for Speaker Recognition
http://arxiv.org/abs/2004.00132
AUTHORS:        João Antônio Chagas Nunes ; David Macêdo ; Cleber Zanchettin
HIGHLIGHT:      To address this demand, we propose a portable model called Additive Margin MobileNet1D (AM-MobileNet1D) to Speaker Identification on mobile devices.

76, TITLE:      EOLO: Embedded Object Segmentation only Look Once
http://arxiv.org/abs/2004.00123
AUTHORS:        Longfei Zeng ; Mohammed Sabah
COMMENTS:       7 pages, 5 figures, 2 tables, 25 conferences
HIGHLIGHT:      In this paper, we introduce an anchor-free and single-shot instance segmentation method, which is conceptually simple with 3 independent branches, fully convolutional and can be used by easily embedding it into mobile and embedded devices.

77, TITLE:      Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds
http://arxiv.org/abs/2004.00186
AUTHORS:        Guodong Xu ; Wenxiao Wang ; Zili Liu ; Liang Xie ; Zheng Yang ; Haifeng Liu ; Deng Cai
HIGHLIGHT:      Some methods propose localizing 3D objects directly from raw point clouds to avoid information loss.

78, TITLE:      HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation
http://arxiv.org/abs/2004.00060
AUTHORS:        Bardia Doosti ; Shujon Naha ; Majid Mirbagheri ; David Crandall
COMMENTS:       IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
HIGHLIGHT:      In this paper, we propose a lightweight model called HOPE-Net which jointly estimates hand and object pose in 2D and 3D in real-time.

79, TITLE:      Optimal Bidding Strategy without Exploration in Real-time Bidding
http://arxiv.org/abs/2004.00100
AUTHORS:        Aritra Ghosh ; Saayan Mitra ; Somdeb Sarkhel ; Viswanathan Swaminathan
COMMENTS:       SIAM SDM 2020. Added supplementary material
HIGHLIGHT:      We exploit two conditional independence structures in the sequential bidding process that allow us to propose a novel practical framework using the maximum entropy principle to imitate the behavior of the true distribution observed in real-time traffic.

80, TITLE:      Information Leakage in Embedding Models
http://arxiv.org/abs/2004.00053
AUTHORS:        Congzheng Song ; Ananth Raghunathan
HIGHLIGHT:      We develop three classes of attacks to systematically study information that might be leaked by embeddings.

81, TITLE:      3D Deep Learning on Medical Images: A Review
http://arxiv.org/abs/2004.00218
AUTHORS:        Satya P. Singh ; Lipo Wang ; Sukrit Gupta ; Haveesh Goli ; Parasuraman Padmanabhan ; Balázs Gulyás
COMMENTS:       13 pages, 4 figures, 2 tables, submitted to IEEE Access
HIGHLIGHT:      In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, brief mathematical description of 3D CNN and the preprocessing steps required for medical images before feeding them to 3D CNNs.

82, TITLE:      Conditional Channel Gated Networks for Task-Aware Continual Learning
http://arxiv.org/abs/2004.00070
AUTHORS:        Davide Abati ; Jakub Tomczak ; Tijmen Blankevoort ; Simone Calderara ; Rita Cucchiara ; Babak Ehteshami Bejnordi
COMMENTS:       CVPR 2020 (oral)
HIGHLIGHT:      In this work, we introduce a novel framework to tackle this problem with conditional computation.

83, TITLE:      MetaPoison: Practical General-purpose Clean-label Data Poisoning
http://arxiv.org/abs/2004.00225
AUTHORS:        W. Ronny Huang ; Jonas Geiping ; Liam Fowl ; Gavin Taylor ; Tom Goldstein
COMMENTS:       First two authors contributed equally
HIGHLIGHT:      Instead, we pose crafting poisons more generally as a bi-level optimization problem, where the inner level corresponds to training a network on a poisoned dataset and the outer level corresponds to updating those poisons to achieve a desired behavior on the trained model.

84, TITLE:      Ontology-based Interpretable Machine Learning for Textual Data
http://arxiv.org/abs/2004.00204
AUTHORS:        Phung Lai ; NhatHai Phan ; Han Hu ; Anuja Badeti ; David Newman ; Dejing Dou
COMMENTS:       Accepted by IJCNN 2020
HIGHLIGHT:      In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models.

85, TITLE:      A theory of independent mechanisms for extrapolation in generative models
http://arxiv.org/abs/2004.00184
AUTHORS:        Michel Besserve ; Rémy Sun ; Dominik Janzing ; Bernhard Schölkopf
COMMENTS:       19 pages
HIGHLIGHT:      We develop a framework to formalize this intuition, using the principle of Independent Causal Mechanisms, and show how over-parameterization of generative neural networks can hinder extrapolation capabilities.

86, TITLE:      Improvement of electronic Governance and mobile Governance in Multilingual Countries with Digital Etymology using Sanskrit Grammar
http://arxiv.org/abs/2004.00104
AUTHORS:        Arijit Das ; Diganta Saha
COMMENTS:       7 pages. 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, 2017
HIGHLIGHT:      In this research we have sorted out the problems faced by Indo Aryan speaking netizens which is in general also applicable to any language family groups or subgroups.

87, TITLE:      Deep Entity Matching with Pre-Trained Language Models
http://arxiv.org/abs/2004.00584
AUTHORS:        Yuliang Li ; Jinfeng Li ; Yoshihiko Suhara ; AnHai Doan ; Wang-Chiew Tan
COMMENTS:       In Submission to VLDB 2021
HIGHLIGHT:      We present Ditto, a novel entity matching system based on pre-trained Transformer-based language models.

88, TITLE:      Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
http://arxiv.org/abs/2004.00530
AUTHORS:        Zhuangdi Zhu ; Kaixiang Lin ; Bo Dai ; Jiayu Zhou
HIGHLIGHT:      In this work, we propose Self-Adaptive Imitation Learning (SAIL) that can achieve (near) optimal performance given only a limited number of sub-optimal demonstrations for highly challenging sparse reward tasks.

89, TITLE:      Statistical Queries and Statistical Algorithms: Foundations and Applications
http://arxiv.org/abs/2004.00557
AUTHORS:        Lev Reyzin
COMMENTS:       21 pages
HIGHLIGHT:      We introduce the model, give the main definitions, and we explore the fundamental theory statistical queries and how how it connects to various notions of learnability.

90, TITLE:      Automated Configuration of Negotiation Strategies
http://arxiv.org/abs/2004.00094
AUTHORS:        Bram M. Renting ; Holger H. Hoos ; Catholijn M. Jonker
COMMENTS:       Appears in Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)
HIGHLIGHT:      For this purpose, we developed a method leveraging automated algorithm configuration to find the best strategies for a specific set of negotiation settings.

91, TITLE:      OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
http://arxiv.org/abs/2004.00348
AUTHORS:        Irene Vlassi Pandi ; Earl T. Barr ; Andrew D. Gordon ; Charles Sutton
HIGHLIGHT:      We present a new approach to the type inference problem for dynamic languages.

92, TITLE:      Streaming Temporal Graphs: Subgraph Matching
http://arxiv.org/abs/2004.00215
AUTHORS:        Eric L. Goodman ; Dirk Grunwald
COMMENTS:       Big Data 2019
HIGHLIGHT:      We present a high-level language for describing temporal subgraphs of interest, the Streaming Analytics Language (SAL).

93, TITLE:      FreezeML: Complete and Easy Type Inference for First-Class Polymorphism
http://arxiv.org/abs/2004.00396
AUTHORS:        Frank Emrich ; Sam Lindley ; Jan Stolarek ; James Cheney ; Jonathan Coates
COMMENTS:       48 pages, 23 Figures. Accepted for PLDI 2020
HIGHLIGHT:      We put forth a new proposal, FreezeML, a conservative extension of ML with two new features.

94, TITLE:      Crafty: Efficient, HTM-Compatible Persistent Transactions
http://arxiv.org/abs/2004.00262
AUTHORS:        Kaan Genç ; Michael D. Bond ; Guoqing Harry Xu
COMMENTS:       34 pages, 25 figures. To be published 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.

95, TITLE:      Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms
http://arxiv.org/abs/2004.00038
AUTHORS:        Halgurd S. Maghdid ; Aras T. Asaad ; Kayhan Zrar Ghafoor ; Ali Safaa Sadiq ; Muhammad Khurram Khan
COMMENTS:       8 pages, 8 figures
HIGHLIGHT:      In this work, we focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner.

96, TITLE:      Improved RawNet with Filter-wise Rescaling for Text-independent Speaker Verification using Raw Waveforms
http://arxiv.org/abs/2004.00526
AUTHORS:        Jee-weon Jung ; Seung-bin Kim ; Hye-jin Shim ; Ju-ho Kim ; Ha-Jin Yu
COMMENTS:       5 pages, 1 figure, 5 tables, submitted to Interspeech 2020 as a conference paper
HIGHLIGHT:      In this study, we improve RawNet by rescaling feature maps using various methods.



==========Updates to Previous Papers==========
1, TITLE:       Multiscale modelling and simulation of physical systems as semiosis
http://arxiv.org/abs/2003.11370
AUTHORS:        Martin Thomas Horsch ; Silvia Chiacchiera ; Michael A. Seaton ; Ilian T. Todorov
COMMENTS:       We need to remove this submission due to a disagreement from project partners whose approval would be required
HIGHLIGHT:      It is explored how physicalist mereotopology and Peircean semiotics can be applied to represent models, simulations, and workflows in multiscale modelling and simulation of physical systems within a top-level ontology.

2, TITLE:       Collective Entity Alignment via Adaptive Features
http://arxiv.org/abs/1912.08404
AUTHORS:        Weixin Zeng ; Xiang Zhao ; Jiuyang Tang ; Xuemin Lin
COMMENTS:       ICDE20
HIGHLIGHT:      To fill this gap, we propose a collective EA framework.

3, TITLE:       Weighting NTBEA for Game AI Optimisation
http://arxiv.org/abs/2003.10378
AUTHORS:        James Goodman ; Simon Lucas
HIGHLIGHT:      We introduce weighting functions to the model to obtain Weighted- NTBEA and test this on four benchmark functions and two game environments.

4, TITLE:       Improving Face Recognition from Hard Samples via Distribution Distillation Loss
http://arxiv.org/abs/2002.03662
AUTHORS:        Yuge Huang ; Pengcheng Shen ; Ying Tai ; Shaoxin Li ; Xiaoming Liu ; Jilin Li ; Feiyue Huang ; Rongrong Ji
HIGHLIGHT:      To improve the performance on those hard samples for general tasks, we propose a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is a simple, effective and generic for various types of facial variations.

5, TITLE:       Squeeze-and-Attention Networks for Semantic Segmentation
http://arxiv.org/abs/1909.03402
AUTHORS:        Zilong Zhong ; Zhong Qiu Lin ; Rene Bidart ; Xiaodan Hu ; Ibrahim Ben Daya ; Zhifeng Li ; Wei-Shi Zheng ; Jonathan Li ; Alexander Wong
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we propose a novel squeeze-and-attention network (SANet) architecture that leverages an effective squeeze-and-attention (SA) module to account for two distinctive characteristics of segmentation: i) pixel-group attention, and ii) pixel-wise prediction.

6, TITLE:       Evaluating Weakly Supervised Object Localization Methods Right
http://arxiv.org/abs/2001.07437
AUTHORS:        Junsuk Choe ; Seong Joon Oh ; Seungho Lee ; Sanghyuk Chun ; Zeynep Akata ; Hyunjung Shim
COMMENTS:       CVPR 2020 camera-ready. First two authors contributed equally. Code: https://github.com/clovaai/wsolevaluation
HIGHLIGHT:      In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.

7, TITLE:       Distilled Hierarchical Neural Ensembles with Adaptive Inference Cost
http://arxiv.org/abs/2003.01474
AUTHORS:        Adria Ruiz ; Jakob Verbeek
HIGHLIGHT:      In this paper, we propose Hierarchical Neural Ensembles (HNE), a novel framework to embed an ensemble of multiple networks by sharing intermediate layers using a hierarchical structure.

8, TITLE:       Acute Lymphoblastic Leukemia Classification from Microscopic Images using Convolutional Neural Networks
http://arxiv.org/abs/1906.09020
AUTHORS:        Jonas Prellberg ; Oliver Kramer
HIGHLIGHT:      We present a simple, yet effective classification approach using a ResNeXt convolutional neural network with Squeeze-and-Excitation modules.

9, TITLE:       Appraisal Theories for Emotion Classification in Text
http://arxiv.org/abs/2003.14155
AUTHORS:        Jan Hofmann ; Enrica Troiano ; Kai Sassenberg ; Roman Klinger
HIGHLIGHT:      With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events and show their potential for emotion classification when being encoded in classification models.

10, TITLE:      Suphx: Mastering Mahjong with Deep Reinforcement Learning
http://arxiv.org/abs/2003.13590
AUTHORS:        Junjie Li ; Sotetsu Koyamada ; Qiwei Ye ; Guoqing Liu ; Chao Wang ; Ruihan Yang ; Li Zhao ; Tao Qin ; Tie-Yan Liu ; Hsiao-Wuen Hon
HIGHLIGHT:      We design an AI for Mahjong, named Suphx, based on deep reinforcement learning with some newly introduced techniques including global reward prediction, oracle guiding, and run-time policy adaptation.

11, TITLE:      Neural Game Engine: Accurate learning of generalizable forward models from pixels
http://arxiv.org/abs/2003.10520
AUTHORS:        Chris Bamford ; Simon Lucas
HIGHLIGHT:      Building upon previous work on the Neural GPU, this paper introduces the Neural Game Engine, as a way to learn models directly from pixels.

12, TITLE:      SpeechBERT: Cross-Modal Pre-trained Language Model for End-to-end Spoken Question Answering
http://arxiv.org/abs/1910.11559
AUTHORS:        Yung-Sung Chuang ; Chi-Liang Liu ; Hung-Yi Lee
HIGHLIGHT:      To bring this advantage of pre-trained language models into spoken question answering, we propose SpeechBERT, a cross-modal transformer-based pre-trained language model.

13, TITLE:      Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data
http://arxiv.org/abs/1912.03681
AUTHORS:        Udaranga Wickramasinghe ; Edoardo Remelli ; Graham Knott ; Pascal Fua
HIGHLIGHT:      In this paper, we therefore introduce a novel architecture that goes directly from 3D image volumes to 3D surfaces without post-processing and with better accuracy than current methods.

14, TITLE:      Recognizing Characters in Art History Using Deep Learning
http://arxiv.org/abs/2003.14171
AUTHORS:        Prathmesh Madhu ; Ronak Kosti ; Lara Mührenberg ; Peter Bell ; Andreas Maier ; Vincent Christlein
HIGHLIGHT:      In this paper, we focus on the problem of recognizing the characters in Art History.

15, TITLE:      GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
http://arxiv.org/abs/1912.05524
AUTHORS:        Prune Truong ; Martin Danelljan ; Radu Timofte
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we propose a universal network architecture that is directly applicable to all the aforementioned dense correspondence problems.

16, TITLE:      Dual Graph Convolutional Network for Semantic Segmentation
http://arxiv.org/abs/1909.06121
AUTHORS:        Li Zhang ; Xiangtai Li ; Anurag Arnab ; Kuiyuan Yang ; Yunhai Tong ; Philip H. S. Torr
COMMENTS:       accepted by BMVC 2019
HIGHLIGHT:      In contrast to previous work that uses multi-scale feature fusion or dilated convolutions, we propose a novel graph-convolutional network (GCN) to address this problem.

17, TITLE:      Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
http://arxiv.org/abs/1902.01338
AUTHORS:        Amelia Jiménez-Sánchez ; Anees Kazi ; Shadi Albarqouni ; Chlodwig Kirchhoff ; Peter Biberthaler ; Nassir Navab ; Sonja Kirchhoff ; Diana Mateus
COMMENTS:       Accepted at IPCAI 2020 and IJCARS
HIGHLIGHT:      The proposed framework aims to improve patient treatment planning and provide support for the training of trauma surgeon residents.

18, TITLE:      Improving land cover segmentation across satellites using domain adaptation
http://arxiv.org/abs/1912.05000
AUTHORS:        Nadir Bengana ; Janne Heikkilä
COMMENTS:       12 pages, Transaction
HIGHLIGHT:      In this paper, we aim at using domain adaptation to solve the aforementioned problems.

19, TITLE:      Dynamic Graph Message Passing Networks
http://arxiv.org/abs/1908.06955
AUTHORS:        Li Zhang ; Dan Xu ; Anurag Arnab ; Philip H. S. Torr
HIGHLIGHT:      We propose a dynamic graph message passing network, based on the message passing neural network framework, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph.

20, TITLE:      Deep Snake for Real-Time Instance Segmentation
http://arxiv.org/abs/2001.01629
AUTHORS:        Sida Peng ; Wen Jiang ; Huaijin Pi ; Xiuli Li ; Hujun Bao ; Xiaowei Zhou
COMMENTS:       Accepted to CVPR 2020 as Oral. Add experiments on MS COCO
HIGHLIGHT:      This paper introduces a novel contour-based approach named deep snake for real-time instance segmentation.

21, TITLE:      A New Meta-Baseline for Few-Shot Learning
http://arxiv.org/abs/2003.04390
AUTHORS:        Yinbo Chen ; Xiaolong Wang ; Zhuang Liu ; Huijuan Xu ; Trevor Darrell
COMMENTS:       Code: https://github.com/cyvius96/few-shot-meta-baseline
HIGHLIGHT:      We present a Meta-Baseline method, by pre-training a classifier on all base classes and meta-learning on a nearest-centroid based few-shot classification algorithm, it outperforms recent state-of-the-art methods by a large margin.

22, TITLE:      Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events
http://arxiv.org/abs/1909.08784
AUTHORS:        Ian Stewart ; Diyi Yang ; Jacob Eisenstein
COMMENTS:       ICWSM 2020
HIGHLIGHT:      We examine how people refer to locations, focusing specifically on contextual descriptors, such as "San Juan" versus "San Juan, Puerto Rico."

23, TITLE:      Learning Cross-lingual Embeddings from Twitter via Distant Supervision
http://arxiv.org/abs/1905.07358
AUTHORS:        Jose Camacho-Collados ; Yerai Doval ; Eugenio Martínez-Cámara ; Luis Espinosa-Anke ; Francesco Barbieri ; Steven Schockaert
COMMENTS:       Accepted to ICWSM 2020. 11 pages, 1 appendix. Pre-trained embeddings available at https://github.com/pedrada88/crossembeddings-twitter
HIGHLIGHT:      In this paper we explore a research direction that has been surprisingly neglected in the literature: leveraging noisy user-generated text to learn cross-lingual embeddings particularly tailored towards social media applications.

24, TITLE:      Adposition and Case Supersenses v2.5: Guidelines for English
http://arxiv.org/abs/1704.02134
AUTHORS:        Nathan Schneider ; Jena D. Hwang ; Archna Bhatia ; Vivek Srikumar ; Na-Rae Han ; Tim O'Gorman ; Sarah R. Moeller ; Omri Abend ; Adi Shalev ; Austin Blodgett ; Jakob Prange
HIGHLIGHT:      This document offers a detailed linguistic description of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al., 2018), an inventory of 50 semantic labels ("supersenses") that characterize the use of adpositions and case markers at a somewhat coarse level of granularity, as demonstrated in the STREUSLE corpus (https://github.com/nert-gu/streusle/; version 4.3 tracks guidelines version 2.5).

25, TITLE:      Evaluating Coherence in Dialogue Systems using Entailment
http://arxiv.org/abs/1904.03371
AUTHORS:        Nouha Dziri ; Ehsan Kamalloo ; Kory W. Mathewson ; Osmar Zaiane
COMMENTS:       5 pages, 2 figures; NAACL-HLT 2019
HIGHLIGHT:      In this paper, we present interpretable metrics for evaluating topic coherence by making use of distributed sentence representations.

26, TITLE:      Rethinking Exposure Bias In Language Modeling
http://arxiv.org/abs/1910.11235
AUTHORS:        Yifan Xu ; Kening Zhang ; Haoyu Dong ; Yuezhou Sun ; Wenlong Zhao ; Zhuowen Tu
HIGHLIGHT:      In this paper, we adopt two simple strategies, multi-range reinforcing, and multi-entropy sampling, to amplify and denoise the reward signal.

27, TITLE:      Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data
http://arxiv.org/abs/2002.03605
AUTHORS:        Jan Kieseler
HIGHLIGHT:      As proof of concept, the object condensation method is applied to a simple object classification problem in images and used to reconstruct multiple particles from detector signals.

28, TITLE:      Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding
http://arxiv.org/abs/2003.03212
AUTHORS:        Seong Hyeon Park ; Gyubok Lee ; Manoj Bhat ; Jimin Seo ; Minseok Kang ; Jonathan Francis ; Ashwin R. Jadhav ; Paul Pu Liang ; Louis-Philippe Morency
HIGHLIGHT:      In this paper, we propose a model that fully synthesizes multiple input signals from the multimodal world|the environment's scene context and interactions between multiple surrounding agents|to best model all diverse and admissible trajectories.

29, TITLE:      Video to Events: Recycling Video Datasets for Event Cameras
http://arxiv.org/abs/1912.03095
AUTHORS:        Daniel Gehrig ; Mathias Gehrig ; Javier Hidalgo-Carrió ; Davide Scaramuzza
HIGHLIGHT:      In this paper, we present a method that addresses these needs by converting any existing video dataset recorded with conventional cameras to synthetic event data.

30, TITLE:      Adaptive Adversarial Attack on Scene Text Recognition
http://arxiv.org/abs/1807.03326
AUTHORS:        Xiaoyong Yuan ; Pan He ; Xiaolin Andy Li ; Dapeng Oliver Wu
COMMENTS:       To be appear in INFOCOM 2020, The Eighth International Workshop on Security and Privacy in Big Data
HIGHLIGHT:      In this work, we speed up adversarial attacks, especially on sequential learning tasks.

31, TITLE:      DPGN: Distribution Propagation Graph Network for Few-shot Learning
http://arxiv.org/abs/2003.14247
AUTHORS:        Ling Yang ; Liangliang Li ; Zilun Zhang ; Xinyu Zhou ; Erjin Zhou ; Yu Liu
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      We propose a novel approach named distribution propagation graph network (DPGN) for few-shot learning.

32, TITLE:      Cross-Shape Graph Convolutional Networks
http://arxiv.org/abs/2003.09053
AUTHORS:        Dmitry Petrov ; Evangelos Kalogerakis
HIGHLIGHT:      We present a method that processes 3D point clouds by performing graph convolution operations across shapes.

33, TITLE:      MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
http://arxiv.org/abs/1907.11922
AUTHORS:        Cheng-Han Lee ; Ziwei Liu ; Lingyun Wu ; Ping Luo
COMMENTS:       To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. The code, models and dataset are available at: https://github.com/switchablenorms/CelebAMask-HQ
HIGHLIGHT:      To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation. To facilitate extensive studies, we construct a large-scale high-resolution face dataset with fine-grained mask annotations named CelebAMask-HQ.

34, TITLE:      TITAN: Future Forecast using Action Priors
http://arxiv.org/abs/2003.13886
AUTHORS:        Srikanth Malla ; Behzad Dariush ; Chiho Choi
COMMENTS:       CVPR 2020 (oral)
HIGHLIGHT:      In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Targeted Action priors Network), a new model that incorporates prior positions, actions, and context to forecast future trajectory of agents and future ego-motion. In the absence of an appropriate dataset for this task, we created the TITAN dataset that consists of 700 labeled video-clips (with odometry) captured from a moving vehicle on highly interactive urban traffic scenes in Tokyo.

35, TITLE:      Graph-guided Architecture Search for Real-time Semantic Segmentation
http://arxiv.org/abs/1909.06793
AUTHORS:        Peiwen Lin ; Peng Sun ; Guangliang Cheng ; Sirui Xie ; Xi Li ; Jianping Shi
COMMENTS:       CVPR2020
HIGHLIGHT:      In order to release researchers from these tedious mechanical trials, we propose a Graph-guided Architecture Search (GAS) pipeline to automatically search real-time semantic segmentation networks.

36, TITLE:      Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data
http://arxiv.org/abs/2001.02799
AUTHORS:        Xi Yan ; David Acuna ; Sanja Fidler
HIGHLIGHT:      We introduce Neural Data Server (NDS), a large-scale search engine for finding the most useful transfer learning data to the target domain.

37, TITLE:      On the Detection of Digital Face Manipulation
http://arxiv.org/abs/1910.01717
AUTHORS:        Hao Dang ; Feng Liu ; Joel Stehouwer ; Xiaoming Liu ; Anil Jain
COMMENTS:       To appear in CVPR 2020
HIGHLIGHT:      Instead of simply using multi-task learning to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize an attention mechanism to process and improve the feature maps for the classification task. To enable our study of manipulated face detection and localization, we collect a large-scale database that contains numerous types of facial forgeries.

38, TITLE:      DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction
http://arxiv.org/abs/1911.09204
AUTHORS:        Jiongchao Jin ; Akshay Gadi Patil ; Zhang Xiong ; Hao Zhang
HIGHLIGHT:      We introduce a differential visual similarity metric to train deep neural networks for 3D reconstruction, aimed at improving reconstruction quality.

39, TITLE:      Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning
http://arxiv.org/abs/1904.10348
AUTHORS:        Yann Labbé ; Sergey Zagoruyko ; Igor Kalevatykh ; Ivan Laptev ; Justin Carpentier ; Mathieu Aubry ; Josef Sivic
COMMENTS:       Accepted for publication in IEEE Robotics and Automation Letters (RA-L)
HIGHLIGHT:      To do so, we introduce a complete pipeline relying on two key contributions.

40, TITLE:      Flow Contrastive Estimation of Energy-Based Models
http://arxiv.org/abs/1912.00589
AUTHORS:        Ruiqi Gao ; Erik Nijkamp ; Diederik P. Kingma ; Zhen Xu ; Andrew M. Dai ; Ying Nian Wu
HIGHLIGHT:      (1) The update of the energy-based model is based on noise contrastive estimation, with the flow model serving as a strong noise distribution.

41, TITLE:      Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
http://arxiv.org/abs/2002.01180
AUTHORS:        Arun Pandey ; Joachim Schreurs ; Johan A. K. Suykens
HIGHLIGHT:      In this paper, we introduce weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs).

42, TITLE:      Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks
http://arxiv.org/abs/1911.07883
AUTHORS:        Fengda Zhu ; Yi Zhu ; Xiaojun Chang ; Xiaodan Liang
HIGHLIGHT:      In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to take advantage of the additional training signals derived from the semantic information.

43, TITLE:      Towards Segmenting Anything That Moves
http://arxiv.org/abs/1902.03715
AUTHORS:        Achal Dave ; Pavel Tokmakov ; Deva Ramanan
COMMENTS:       Website: http://www.achaldave.com/projects/anything-that-moves/. Code: https://github.com/achalddave/segment-any-moving
HIGHLIGHT:      To bridge this gap, we propose a simple learning-based approach for spatio-temporal grouping. To address this concern, we propose two new benchmarks for generic, moving object detection, and show that our model matches top-down methods on common categories, while significantly out-performing both top-down and bottom-up methods on never-before-seen categories.

44, TITLE:      TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting
http://arxiv.org/abs/2003.14401
AUTHORS:        Zhuoqian Yang ; Wentao Zhu ; Wayne Wu ; Chen Qian ; Qiang Zhou ; Bolei Zhou ; Chen Change Loy
COMMENTS:       Accepted by CVPR 2020. The first three authors contributed equally. Project page: https://yzhq97.github.io/transmomo/
HIGHLIGHT:      We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person.

45, TITLE:      Dynamic Convolution: Attention over Convolution Kernels
http://arxiv.org/abs/1912.03458
AUTHORS:        Yinpeng Chen ; Xiyang Dai ; Mengchen Liu ; Dongdong Chen ; Lu Yuan ; Zicheng Liu
COMMENTS:       CVPR 2020 (Oral)
HIGHLIGHT:      To address this issue, we present Dynamic Convolution, a new design that increases model complexity without increasing the network depth or width.

46, TITLE:      Accelerated physical emulation of Bayesian inference in spiking neural networks
http://arxiv.org/abs/1807.02389
AUTHORS:        Akos F. Kungl ; Sebastian Schmitt ; Johann Klähn ; Paul Müller ; Andreas Baumbach ; Dominik Dold ; Alexander Kugele ; Nico Gürtler ; Luziwei Leng ; Eric Müller ; Christoph Koke ; Mitja Kleider ; Christian Mauch ; Oliver Breitwieser ; Maurice Güttler ; Dan Husmann ; Kai Husmann ; Joscha Ilmberger ; Andreas Hartel ; Vitali Karasenko ; Andreas Grübl ; Johannes Schemmel ; Karlheinz Meier ; Mihai A. Petrovici
COMMENTS:       This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi: 10.3389/fnins.2019.01201
HIGHLIGHT:      We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data.

47, TITLE:      RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
http://arxiv.org/abs/2003.01811
AUTHORS:        Bing Han ; Gopalakrishnan Srinivasan ; Kaushik Roy
COMMENTS:       to be published in CVPR'20
HIGHLIGHT:      We propose ANN-SNN conversion using "soft reset" spiking neuron model, referred to as Residual Membrane Potential (RMP) spiking neuron, which retains the "residual" membrane potential above threshold at the firing instants.

48, TITLE:      Distributed Embodied Evolution in Networks of Agents
http://arxiv.org/abs/2003.12848
AUTHORS:        Anil Yaman ; Giovanni Iacca
HIGHLIGHT:      In this work we propose a distributed embodied evolutionary approach to optimize spatially distributed, locally interacting agents by allowing them to exchange their behavior parameters and learn from each other to adapt to a certain task within a given environment.

49, TITLE:      End-to-end deep learning for big data analytics under a quasi-open set assumption
http://arxiv.org/abs/2002.01368
AUTHORS:        Emile R. Engelbrecht ; Johan A. du Preez
COMMENTS:       12 Pages
HIGHLIGHT:      Generally, big data classification models are trained using a semi-supervised learning framework due to the available unlabelled samples and the high cost to gather labelled samples.

50, TITLE:      Learning in the Frequency Domain
http://arxiv.org/abs/2002.12416
AUTHORS:        Kai Xu ; Minghai Qin ; Fei Sun ; Yuhao Wang ; Yen-Kuang Chen ; Fengbo Ren
COMMENTS:       Accepted to CVPR 2020; https://github.com/calmevtime/DCTNet
HIGHLIGHT:      Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed without accuracy loss.

51, TITLE:      Looking at the right stuff: Guided semantic-gaze for autonomous driving
http://arxiv.org/abs/1911.10455
AUTHORS:        Anwesan Pal ; Sayan Mondal ; Henrik I. Christensen
COMMENTS:       Paper accepted at CVPR-2020
HIGHLIGHT:      We propose a novel Semantics Augmented GazE (SAGE) detection approach that captures driving specific contextual information, in addition to the raw gaze.

52, TITLE:      Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data
http://arxiv.org/abs/2002.11297
AUTHORS:        Yen-Chang Hsu ; Yilin Shen ; Hongxia Jin ; Zsolt Kira
COMMENTS:       CVPR 2020
HIGHLIGHT:      We specifically propose to decompose confidence scoring as well as a modified input pre-processing method.

53, TITLE:      EfficientDet: Scalable and Efficient Object Detection
http://arxiv.org/abs/1911.09070
AUTHORS:        Mingxing Tan ; Ruoming Pang ; Quoc V. Le
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency.

54, TITLE:      Safety-Aware Hardening of 3D Object Detection Neural Network Systems
http://arxiv.org/abs/2003.11242
AUTHORS:        Chih-Hong Cheng
COMMENTS:       This version is similar to v1 with an added statement: "The evaluation using KITTI dataset in this paper is for knowledge dissemination and scientific publication and is not for commercial use"
HIGHLIGHT:      Subsequently, we introduce a specialized loss function reflecting (1) the safety specification, (2) the use of single-stage detection architecture, and finally, (3) the characterization of robustness under perturbation.

55, TITLE:      Analysing the Extent of Misinformation in Cancer Related Tweets
http://arxiv.org/abs/2003.13657
AUTHORS:        Rakesh Bal ; Sayan Sinha ; Swastika Dutta ; Risabh Joshi ; Sayan Ghosh ; Ritam Dutt
COMMENTS:       Proceedings of the 14th International Conference on Web and Social Media (ICWSM-20)
HIGHLIGHT:      In this work, we aim to tackle the misinformation spread in such platforms. We collect and present a dataset regarding tweets which talk specifically about cancer and propose an attention-based deep learning model for automated detection of misinformation along with its spread.

56, TITLE:      Network Representation Learning for Link Prediction: Are we improving upon simple heuristics?
http://arxiv.org/abs/2002.11522
AUTHORS:        Alexandru Mara ; Jefrey Lijffijt ; Tijl De Bie
HIGHLIGHT:      In this work, we analyse 17 network embedding methods on 7 real-world datasets and find, using a consistent evaluation pipeline, only thin progress over the recent years.

57, TITLE:      Hierarchical Scene Coordinate Classification and Regression for Visual Localization
http://arxiv.org/abs/1909.06216
AUTHORS:        Xiaotian Li ; Shuzhe Wang ; Yi Zhao ; Jakob Verbeek ; Juho Kannala
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image.

58, TITLE:      Neural Word Search in Historical Manuscript Collections
http://arxiv.org/abs/1812.02771
AUTHORS:        Tomas Wilkinson ; Jonas Lindström ; Anders Brun
COMMENTS:       Extension of arXiv:1703.07645. This version adds results on two additional benchmark datasets (Botany and Konzilsprotokolle) and improves the experiment done in section 5.3.1
HIGHLIGHT:      We address the problem of segmenting and retrieving word images in collections of historical manuscripts given a text query.

59, TITLE:      Weakly Supervised Visual Semantic Parsing
http://arxiv.org/abs/2001.02359
AUTHORS:        Alireza Zareian ; Svebor Karaman ; Shih-Fu Chang
COMMENTS:       To be presented at CVPR 2020 (oral paper)
HIGHLIGHT:      In this paper, we address those two limitations by first proposing a generalized formulation of SGG, namely Visual Semantic Parsing, which disentangles entity and predicate recognition, and enables sub-quadratic performance.

60, TITLE:      Squeezed Deep 6DoF Object Detection Using Knowledge Distillation
http://arxiv.org/abs/2003.13586
AUTHORS:        Heitor Felix ; Walber M. Rodrigues ; David Macêdo ; Francisco Simões ; Adriano L. I. Oliveira ; Veronica Teichrieb ; Cleber Zanchettin
COMMENTS:       This paper was accepted by IJCNN 2020
HIGHLIGHT:      In this paper, we propose an approach to reduce the complexity of 6DoF detection networks while maintaining accuracy.

61, TITLE:      Depth Completion using a View Constrained Deep Prior
http://arxiv.org/abs/2001.07791
AUTHORS:        Pallabi Ghosh ; Vibhav Vineet ; Larry S. Davis ; Abhinav Shrivastava ; Sudipta Sinha ; Neel Joshi
HIGHLIGHT:      Recent work has shown that the structure of convolutional neural networks (CNNs) induces a strong prior that favors natural images.

62, TITLE:      SAL: Sign Agnostic Learning of Shapes from Raw Data
http://arxiv.org/abs/1911.10414
AUTHORS:        Matan Atzmon ; Yaron Lipman
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper we introduce Sign Agnostic Learning (SAL), a deep learning approach for learning implicit shape representations directly from raw, unsigned geometric data, such as point clouds and triangle soups.

63, TITLE:      Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction
http://arxiv.org/abs/1912.01756
AUTHORS:        Fuyang Zhang ; Nelson Nauata ; Yasutaka Furukawa
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      This paper proposes a novel message passing neural (MPN) architecture Conv-MPN, which reconstructs an outdoor building as a planar graph from a single RGB image.

64, TITLE:      Lemotif: An Affective Visual Journal Using Deep Neural Networks
http://arxiv.org/abs/1903.07766
AUTHORS:        X. Alice Li ; Devi Parikh
HIGHLIGHT:      We present Lemotif, an integrated natural language processing and image generation system that uses machine learning to (1) parse a text-based input journal entry describing the user's day for salient themes and emotions and (2) visualize the detected themes and emotions in creative and appealing image motifs.

65, TITLE:      Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation
http://arxiv.org/abs/1906.06196
AUTHORS:        Jean Kossaifi ; Antoine Toisoul ; Adrian Bulat ; Yannis Panagakis ; Timothy Hospedales ; Maja Pantic
COMMENTS:       IEEE CVPR 2020
HIGHLIGHT:      In this paper, we unify these two approaches by proposing a tensor factorization framework for efficient multidimensional (separable) convolutions of higher-order.

66, TITLE:      Learning Generalisable Omni-Scale Representations for Person Re-Identification
http://arxiv.org/abs/1910.06827
AUTHORS:        Kaiyang Zhou ; Yongxin Yang ; Andrea Cavallaro ; Tao Xiang
COMMENTS:       Extension of conference version: arXiv:1905.00953. Source code: https://github.com/KaiyangZhou/deep-person-reid. Update: fixed typos for results on msmt->market/duke (table 9)
HIGHLIGHT:      In this paper, we develop novel CNN architectures to address both challenges.

67, TITLE:      Attacking Neural Text Detectors
http://arxiv.org/abs/2002.11768
AUTHORS:        Max Wolff
COMMENTS:       Accepted at the ICLR 2020 workshop "Towards Trustworthy ML: Rethinking Security and Privacy for ML."
HIGHLIGHT:      This paper presents two classes of black-box attacks on these detectors, one which randomly replaces characters with homoglyphs, and the other a simple scheme to purposefully misspell words.

68, TITLE:      Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
http://arxiv.org/abs/1906.03393
AUTHORS:        Tengyang Xie ; Yifei Ma ; Yu-Xiang Wang
COMMENTS:       Published at the Neural Information Processing Systems (NeurIPS) 2019
HIGHLIGHT:      Motivated by the many real-world applications of reinforcement learning (RL) that require safe-policy iterations, we consider the problem of off-policy evaluation (OPE) -- the problem of evaluating a new policy using the historical data obtained by different behavior policies -- under the model of nonstationary episodic Markov Decision Processes (MDP) with a long horizon and a large action space.

69, TITLE:      Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks
http://arxiv.org/abs/1911.09737
AUTHORS:        Saurabh Singh ; Shankar Krishnan
HIGHLIGHT:      In this paper we propose the Filter Response Normalization (FRN) layer, a novel combination of a normalization and an activation function, that can be used as a replacement for other normalizations and activations.

70, TITLE:      SNAS: Stochastic Neural Architecture Search
http://arxiv.org/abs/1812.09926
AUTHORS:        Sirui Xie ; Hehui Zheng ; Chunxiao Liu ; Liang Lin
COMMENTS:       ICLR 2019
HIGHLIGHT:      In this work, NAS is reformulated as an optimization problem on parameters of a joint distribution for the search space in a cell.

71, TITLE:      KSR: A Semantic Representation of Knowledge Graph within a Novel Unsupervised Paradigm
http://arxiv.org/abs/1608.07685
AUTHORS:        Han Xiao ; Minlie Huang ; Xiaoyan Zhu
COMMENTS:       submitting to IJCAI 2018
HIGHLIGHT:      To this end, this paper proposes a semantic representation method for knowledge graph \textbf{(KSR)}, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple.

72, TITLE:      Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline
http://arxiv.org/abs/2001.07076
AUTHORS:        Tao Chen ; Rami Bahsoon ; Xin Yao
COMMENTS:       Accepted manuscript to the Proceedings of the IEEE. Please use the following citation: Tao Chen, Rami Bahsoon, and Xin Yao. 2020. Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline. Proc. IEEE, in press
HIGHLIGHT:      In particular, we present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers to perform difficulty and benefit analysis on possible synergies, in an attempt to keep "engineers-in-the-loop".

73, TITLE:      Tree bark re-identification using a deep-learning feature descriptor
http://arxiv.org/abs/1912.03221
AUTHORS:        Martin Robert ; Patrick Dallaire ; Philippe Giguère
HIGHLIGHT:      We thus propose to use data-driven descriptors trained on bark images for tree surface re-identification. To this effect, we collected a large dataset containing 2,400 bark images with strong illumination changes, annotated by surface and with the ability to pixel-align them.

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

75, TITLE:      Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM
http://arxiv.org/abs/1911.09315
AUTHORS:        Alberto Barbado ; Óscar Corcho
COMMENTS:       23 pages, 18 figures
HIGHLIGHT:      Together with that, we propose algorithms to compute metrics related with XAI regarding the "comprehensivility", "representativeness", "stability" and "diversity" of the rules extracted.

76, TITLE:      Evolving Neural Networks through a Reverse Encoding Tree
http://arxiv.org/abs/2002.00539
AUTHORS:        Haoling Zhang ; Chao-Han Huck Yang ; Hector Zenil ; Narsis A. Kiani ; Yue Shen ; Jesper N. Tegner
COMMENTS:       Accepted to IEEE Congress on Evolutionary Computation (IEEE CEC) 2020. Lecture Presentation
HIGHLIGHT:      This paper advances a method which incorporates a type of topological edge coding, named Reverse Encoding Tree (RET), for evolving scalable neural networks efficiently.

77, TITLE:      Correctness of Automatic Differentiation via Diffeologies and Categorical Gluing
http://arxiv.org/abs/2001.02209
AUTHORS:        Mathieu Huot ; Sam Staton ; Matthijs Vákár
COMMENTS:       Proceedings of FoSSaCS 2020
HIGHLIGHT:      We present semantic correctness proofs of Automatic Differentiation (AD).