2020.04.03.txt

==========New Papers==========
1, TITLE:       Mapping Languages: The Corpus of Global Language Use
http://arxiv.org/abs/2004.00798
AUTHORS:        Jonathan Dunn
COMMENTS:       This is a pre-print of an article published in Language Resources and Evaluation. The final authenticated version is available online at: https://doi.org/10.1007/s10579-020-09489-2
HIGHLIGHT:      This paper describes a web-based corpus of global language use with a focus on how this corpus can be used for data-driven language mapping.

2, TITLE:       Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
http://arxiv.org/abs/2004.01178
AUTHORS:        Yunxuan Wei ; Shuhang Gu ; Yawei Li ; Longcun Jin
COMMENTS:       Code will be available at https://github.com/ShuhangGu/DASR
HIGHLIGHT:      In this paper, we propose a novel domain-distance aware super-resolution (DASR) approach for unsupervised real-world image SR.

3, TITLE:       Igbo-English Machine Translation: An Evaluation Benchmark
http://arxiv.org/abs/2004.00648
AUTHORS:        Ignatius Ezeani ; Paul Rayson ; Ikechukwu Onyenwe ; Chinedu Uchechukwu ; Mark Hepple
COMMENTS:       4 pages
HIGHLIGHT:      In this work, we discuss our effort toward building a standard machine translation benchmark dataset for Igbo, one of the 3 major Nigerian languages.

4, TITLE:       Learning to See Through Obstructions
http://arxiv.org/abs/2004.01180
AUTHORS:        Yu-Lun Liu ; Wei-Sheng Lai ; Ming-Hsuan Yang ; Yung-Yu Chuang ; Jia-Bin Huang
COMMENTS:       CVPR 2020. Project page: https://www.cmlab.csie.ntu.edu.tw/~yulunliu/ObstructionRemoval Code: https://github.com/alex04072000/ObstructionRemoval
HIGHLIGHT:      We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.

5, TITLE:       Mapping Languages and Demographics with Georeferenced Corpora
http://arxiv.org/abs/2004.00809
AUTHORS:        Jonathan Dunn ; Ben Adams
COMMENTS:       Proceedings of GeoComputation 19
HIGHLIGHT:      This paper evaluates large georeferenced corpora, taken from both web-crawled and social media sources, against ground-truth population and language-census datasets.

6, TITLE:       Synchronizing Probability Measures on Rotations via Optimal Transport
http://arxiv.org/abs/2004.00663
AUTHORS:        Tolga Birdal ; Michael Arbel ; Umut Şimşekli ; Leonidas Guibas
COMMENTS:       Accepted for publication at CVPR 2020, includes supplementary material. Project website: https://github.com/SynchInVision/probsync
HIGHLIGHT:      We propose a nonparametric Riemannian particle optimization approach to solve the problem.

7, TITLE:       Adversarial Learning for Personalized Tag Recommendation
http://arxiv.org/abs/2004.00698
AUTHORS:        Erik Quintanilla ; Yogesh Rawat ; Andrey Sakryukin ; Mubarak Shah ; Mohan Kankanhalli
HIGHLIGHT:      In this paper, we address the problem of personalized tag recommendation and propose an end-to-end deep network which can be trained on large-scale datasets.

8, TITLE:       Memory-Efficient Incremental Learning Through Feature Adaptation
http://arxiv.org/abs/2004.00713
AUTHORS:        Ahmet Iscen ; Jeffrey Zhang ; Svetlana Lazebnik ; Cordelia Schmid
HIGHLIGHT:      In this work we introduce an approach for incremental learning, which preserves feature descriptors instead of images unlike most existing work.

9, TITLE:       Generalized Zero-Shot Learning Via Over-Complete Distribution
http://arxiv.org/abs/2004.00666
AUTHORS:        Rohit Keshari ; Richa Singh ; Mayank Vatsa
COMMENTS:       9 pages, 5 figures, Accepted in CVPR 2020
HIGHLIGHT:      To learn a discriminative classifier which yields good performance in Zero-Shot Learning (ZSL) settings, we propose to generate an Over-Complete Distribution (OCD) using Conditional Variational Autoencoder (CVAE) of both seen and unseen classes.

10, TITLE:      Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
http://arxiv.org/abs/2004.00705
AUTHORS:        Luming Tang ; Davis Wertheimer ; Bharath Hariharan
COMMENTS:       To appear in CVPR 2020
HIGHLIGHT:      Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition

11, TITLE:      Robust Single Rotation Averaging
http://arxiv.org/abs/2004.00732
AUTHORS:        Seong Hun Lee ; Javier Civera
HIGHLIGHT:      We propose a novel method for single rotation averaging using the Weiszfeld algorithm.

12, TITLE:      Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)
http://arxiv.org/abs/2004.01077
AUTHORS:        Arturo Marban ; Daniel Becking ; Simon Wiedemann ; Wojciech Samek
COMMENTS:       Under review. Code is available at https://github.com/d-becking/efficientCNNs
HIGHLIGHT:      To address this problem, we propose Entropy-Constrained Trained Ternarization (EC2T), a general framework to create sparse and ternary neural networks which are efficient in terms of storage (e.g., at most two binary-masks and two full-precision values are required to save a weight matrix) and computation (e.g., MAC operations are reduced to a few accumulations plus two multiplications).

13, TITLE:      Learning to cooperate: Emergent communication in multi-agent navigation
http://arxiv.org/abs/2004.01097
AUTHORS:        Ivana Kajić ; Eser Aygün ; Doina Precup
HIGHLIGHT:      Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans.

14, TITLE:      Neuronal Sequence Models for Bayesian Online Inference
http://arxiv.org/abs/2004.00930
AUTHORS:        Sascha Frölich ; Dimitrije Marković ; Stefan J. Kiebel
HIGHLIGHT:      We propose that describing sequential neuronal activity as an expression of probabilistic inference over sequences may lead to novel perspectives on brain function.

15, TITLE:      Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning
http://arxiv.org/abs/2004.01098
AUTHORS:        Weichao Mao ; Kaiqing Zhang ; Erik Miehling ; Tamer Başar
COMMENTS:       Submitted to CDC 2020
HIGHLIGHT:      In this work, we investigate a partially observable MARL problem in which agents are cooperative.

16, TITLE:      Improving Confidence in the Estimation of Values and Norms
http://arxiv.org/abs/2004.01056
AUTHORS:        Luciano Cavalcante Siebert ; Rijk Mercuur ; Virginia Dignum ; Jeroen van den Hoven ; Catholijn Jonker
COMMENTS:       16 pages, 3 figures, pre-print for the International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE), co-located with AAMAS 2020
HIGHLIGHT:      We present two methods to reduce ambiguity in profiling the SHAs: one based on search space exploration and another based on counterfactual analysis.

17, TITLE:      Benchmarking End-to-End Behavioural Cloning on Video Games
http://arxiv.org/abs/2004.00981
AUTHORS:        Anssi Kanervisto ; Joonas Pussinen ; Ville Hautamäki
HIGHLIGHT:      We take a step towards a general approach and study the general applicability of behavioural cloning on twelve video games, including six modern video games (published after 2010), by using human demonstrations as training data.

18, TITLE:      Software Language Comprehension using a Program-Derived Semantic Graph
http://arxiv.org/abs/2004.00768
AUTHORS:        Roshni G. Iyer ; Yizhou Sun ; Wei Wang ; Justin Gottschlich
HIGHLIGHT:      In this paper, we describe the PSG and its fundamental structural differences to the Aroma's SPT.

19, TITLE:      Bias in Machine Learning What is it Good (and Bad) for?
http://arxiv.org/abs/2004.00686
AUTHORS:        Thomas Hellström ; Virginia Dignum ; Suna Bensch
HIGHLIGHT:      This paper proposes a taxonomy of these different meanings, terminology, and definitions by surveying the, primarily scientific, literature on machine learning.

20, TITLE:      Action Space Shaping in Deep Reinforcement Learning
http://arxiv.org/abs/2004.00980
AUTHORS:        Anssi Kanervisto ; Christian Scheller ; Ville Hautamäki
HIGHLIGHT:      In this work, we aim to gain insight on these action space modifications by conducting extensive experiments in video-game environments.

21, TITLE:      An anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem
http://arxiv.org/abs/2004.00963
AUTHORS:        Luc Libralesso ; Florian Fontan
HIGHLIGHT:      In this article, we present the anytime tree search algorithm we designed for the 2018 ROADEF/EURO challenge glass cutting problem proposed by the French company Saint-Gobain.

22, TITLE:      Improving 3D Object Detection through Progressive Population Based Augmentation
http://arxiv.org/abs/2004.00831
AUTHORS:        Shuyang Cheng ; Zhaoqi Leng ; Ekin Dogus Cubuk ; Barret Zoph ; Chunyan Bai ; Jiquan Ngiam ; Yang Song ; Benjamin Caine ; Vijay Vasudevan ; Congcong Li ; Quoc V. Le ; Jonathon Shlens ; Dragomir Anguelov
HIGHLIGHT:      We present an algorithm, termed Progressive Population Based Augmentation (PPBA).

23, TITLE:      Occlusion-Aware Depth Estimation with Adaptive Normal Constraints
http://arxiv.org/abs/2004.00845
AUTHORS:        Xiaoxiao Long ; Lingjie Liu ; Christian Theobalt ; Wenping Wang
HIGHLIGHT:      We present a new learning-based method for multi-frame depth estimation from a color video, which is a fundamental problem in scene understanding, robot navigation or handheld 3D reconstruction.

24, TITLE:      Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
http://arxiv.org/abs/2004.00849
AUTHORS:        Zhicheng Huang ; Zhaoyang Zeng ; Bei Liu ; Dongmei Fu ; Jianlong Fu
HIGHLIGHT:      We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding in a unified end-to-end framework.

25, TITLE:      Tracking by Instance Detection: A Meta-Learning Approach
http://arxiv.org/abs/2004.00830
AUTHORS:        Guangting Wang ; Chong Luo ; Xiaoyan Sun ; Zhiwei Xiong ; Wenjun Zeng
COMMENTS:       This paper has been accepted by CVPR'20 as an oral
HIGHLIGHT:      We propose a principled three-step approach to build a high-performance tracker.

26, TITLE:      Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization
http://arxiv.org/abs/2004.00843
AUTHORS:        Marija Vella ; João F. C. Mota
COMMENTS:       Under peer review
HIGHLIGHT:      Based on this observation, we propose to post-process the CNN outputs with an optimization problem that we call TV-TV minimization, which enforces consistency.

27, TITLE:      Sum-product networks: A survey
http://arxiv.org/abs/2004.01167
AUTHORS:        Iago París ; Raquel Sánchez-Cauce ; Francisco Javier Díez
COMMENTS:       24 pages, 6 figures, 97 references
HIGHLIGHT:      This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, the main applications, a brief review of software libraries, and a comparison with related models

28, TITLE:      DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes
http://arxiv.org/abs/2004.01002
AUTHORS:        Jonas Schult ; Francis Engelmann ; Theodora Kontogianni ; Bastian Leibe
COMMENTS:       CVPR 2020 camera-ready version
HIGHLIGHT:      We propose DualConvMesh-Nets (DCM-Net) a family of deep hierarchical convolutional networks over 3D geometric data that combines two types of convolutions.

29, TITLE:      Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
http://arxiv.org/abs/2004.01019
AUTHORS:        Philipp Terhörst ; Jan Niklas Kolf ; Naser Damer ; Florian Kirchbuchner ; Arjan Kuijper
HIGHLIGHT:      In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment.

30, TITLE:      Go Fetch: Mobile Manipulation in Unstructured Environments
http://arxiv.org/abs/2004.00899
AUTHORS:        Kenneth Blomqvist ; Michel Breyer ; Andrei Cramariuc ; Julian Förster ; Margarita Grinvald ; Florian Tschopp ; Jen Jen Chung ; Lionel Ott ; Juan Nieto ; Roland Siegwart
COMMENTS:       Kenneth Blomqvist, Michel Breyer, Andrei Cramariuc, Julian F\"orster, Margarita Grinvald, and Florian Tschopp contributed equally to this work
HIGHLIGHT:      This work presents a mobile manipulation system that combines perception, localization, navigation, motion planning and grasping skills into one common workflow for fetch and carry applications in unstructured indoor environments.

31, TITLE:      Constrained-Space Optimization and Reinforcement Learning for Complex Tasks
http://arxiv.org/abs/2004.00716
AUTHORS:        Ya-Yen Tsai ; Bo Xiao ; Edward Johns ; Guang-Zhong Yang
COMMENTS:       Accepted for publication in RA-Letters and at ICRA 2020
HIGHLIGHT:      This paper presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks.

32, TITLE:      Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens
http://arxiv.org/abs/2004.01029
AUTHORS:        Titas De
HIGHLIGHT:      This study proposes a new method to predict the bone strength of proximal femur specimens from quantitative multi-detector computer tomography (MDCT) images.

33, TITLE:      Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
http://arxiv.org/abs/2004.01179
AUTHORS:        Yu-Lun Liu ; Wei-Sheng Lai ; Yu-Sheng Chen ; Yi-Lung Kao ; Ming-Hsuan Yang ; Yung-Yu Chuang ; Jia-Bin Huang
COMMENTS:       CVPR 2020. Project page: https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR Code: https://github.com/alex04072000/SingleHDR
HIGHLIGHT:      In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge of the LDR image formation pipeline into our model.

34, TITLE:      Fractional Deep Neural Network via Constrained Optimization
http://arxiv.org/abs/2004.00719
AUTHORS:        Harbir Antil ; Ratna Khatri ; Rainald Löhner ; Deepanshu Verma
HIGHLIGHT:      This paper introduces a novel algorithmic framework for a deep neural network (DNN), which in a mathematically rigorous manner, allows us to incorporate history (or memory) into the network -- it ensures all layers are connected to one another.

35, TITLE:      An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition
http://arxiv.org/abs/2004.01110
AUTHORS:        Ehsan Yaghoubi ; Diana Borza ; João Neves ; Aruna Kumar ; Hugo Proença
COMMENTS:       Submitted to Image and Vision Computing journal
HIGHLIGHT:      Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with two-fold contributions: 1) considering the strong semantic correlation between the different full-body attributes, we propose a multi-task deep model that uses an element-wise multiplication layer to extract more comprehensive feature representations.

36, TITLE:      DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes
http://arxiv.org/abs/2004.01170
AUTHORS:        Mahyar Najibi ; Guangda Lai ; Abhijit Kundu ; Zhichao Lu ; Vivek Rathod ; Tom Funkhouser ; Caroline Pantofaru ; David Ross ; Larry S. Davis ; Alireza Fathi
COMMENTS:       To appear in CVPR 2020
HIGHLIGHT:      We propose DOPS, a fast single-stage 3D object detection method for LIDAR data.

37, TITLE:      Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
http://arxiv.org/abs/2004.01176
AUTHORS:        Despoina Paschalidou ; Luc van Gool ; Andreas Geiger
COMMENTS:       To appear at CVPR 2020, project page https://github.com/paschalidoud/hierarchical_primitives
HIGHLIGHT:      We address this challenging problem by proposing a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives as well as their latent hierarchical structure without part-level supervision.

38, TITLE:      Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
http://arxiv.org/abs/2004.01166
AUTHORS:        Henry M. Clever ; Zackory Erickson ; Ariel Kapusta ; Greg Turk ; C. Karen Liu ; Charles C. Kemp
COMMENTS:       18 pages, 18 figures, 5 tables. Accepted for oral presentation at CVPR 2020
HIGHLIGHT:      We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes.

39, TITLE:      Tracking Objects as Points
http://arxiv.org/abs/2004.01177
AUTHORS:        Xingyi Zhou ; Vladlen Koltun ; Philipp Krähenbühl
COMMENTS:       Code available at https://github.com/xingyizhou/CenterTrack
HIGHLIGHT:      In this paper, we present a simultaneous detection and tracking algorithm that is simpler, faster, and more accurate than the state of the art.

40, TITLE:      BUDA: Boundless Unsupervised Domain Adaptation in Semantic Segmentation
http://arxiv.org/abs/2004.01130
AUTHORS:        Maxime Bucher ; Tuan-Hung Vu ; Matthieu Cord ; Patrick Pérez
HIGHLIGHT:      In this work, we define and address "Boundless Unsupervised Domain Adaptation" (BUDA), a novel problem in semantic segmentation.

41, TITLE:      ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
http://arxiv.org/abs/2004.01113
AUTHORS:        Eu Wern Teh ; Terrance DeVries ; Graham W. Taylor
HIGHLIGHT:      We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images.

42, TITLE:      On the Principles of Differentiable Quantum Programming Languages
http://arxiv.org/abs/2004.01122
AUTHORS:        Shaopeng Zhu ; Shih-Han Hung ; Shouvanik Chakrabarti ; Xiaodi Wu
COMMENTS:       Codes are available at https://github.com/LibertasSpZ/adcompile
HIGHLIGHT:      We propose the first formalization of this technique, not only in the context of quantum circuits but also for imperative quantum programs (e.g., with controls), inspired by the success of differentiable programming languages in classical machine learning.

43, TITLE:      Exact and Approximate Methods for Proving Unrealizability of Syntax-Guided Synthesis Problems
http://arxiv.org/abs/2004.00878
AUTHORS:        Qinheping Hu ; John Cyphert ; Loris D'Antoni ; Thomas Reps
HIGHLIGHT:      We consider the problem of automatically establishing that a given syntax-guided-synthesis (SyGuS) problem is unrealizable (i.e., has no solution).

44, TITLE:      Device-aware inference operations in SONOS nonvolatile memory arrays
http://arxiv.org/abs/2004.00802
AUTHORS:        Christopher H. Bennett ; T. Patrick Xiao ; Ryan Dellana ; Vineet Agrawal ; Ben Feinberg ; Venkatraman Prabhakar ; Krishnaswamy Ramkumar ; Long Hinh ; Swatilekha Saha ; Vijay Raghavan ; Ramesh Chettuvetty ; Sapan Agarwal ; Matthew J. Marinella
COMMENTS:       To be presented at IEEE International Physics Reliability Symposium (IRPS) 2020
HIGHLIGHT:      Here, we examine damage caused by these effects, introduce a mitigation strategy, and demonstrate its use in fabricated array of SONOS (Silicon-Oxide-Nitride-Oxide-Silicon) devices.

45, TITLE:      Projected Neural Network for a Class of Sparse Regression with Cardinality Penalty
http://arxiv.org/abs/2004.00858
AUTHORS:        Wenjing Li ; Wei Bian
HIGHLIGHT:      In this paper, we consider a class of sparse regression problems, whose objective function is the summation of a convex loss function and a cardinality penalty.

46, TITLE:      Improving the Utility of Knowledge Graph Embeddings with Calibration
http://arxiv.org/abs/2004.01168
AUTHORS:        Tara Safavi ; Danai Koutra ; Edgar Meij
HIGHLIGHT:      To this end, we propose to calibrate knowledge graph embedding models to output reliable confidence estimates for predicted triples. We also release two resources from our evaluation tasks: An enriched version of the FB15K benchmark and a new knowledge graph dataset extracted from Wikidata.

47, TITLE:      PaStaNet: Toward Human Activity Knowledge Engine
http://arxiv.org/abs/2004.00945
AUTHORS:        Yong-Lu Li ; Liang Xu ; Xinpeng Liu ; Xijie Huang ; Yue Xu ; Shiyi Wang ; Hao-Shu Fang ; Ze Ma ; Mingyang Chen ; Cewu Lu
COMMENTS:       Accepted to CVPR 2020, supplementary materials included, code available: http://hake-mvig.cn/
HIGHLIGHT:      In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics.

48, TITLE:      Learning to Segment the Tail
http://arxiv.org/abs/2004.00900
AUTHORS:        Xinting Hu ; Yi Jiang ; Kaihua Tang ; Jingyuan Chen ; Chunyan Miao ; Hanwang Zhang
HIGHLIGHT:      We propose a "divide\&conquer" strategy for the challenging LVIS task: divide the whole data into balanced parts and then apply incremental learning to conquer each one.

49, TITLE:      Controllable Orthogonalization in Training DNNs
http://arxiv.org/abs/2004.00917
AUTHORS:        Lei Huang ; Li Liu ; Fan Zhu ; Diwen Wan ; Zehuan Yuan ; Bo Li ; Ling Shao
COMMENTS:       Accepted to CVPR 2020. The Code is available at https://github.com/huangleiBuaa/ONI
HIGHLIGHT:      This paper proposes a computationally efficient and numerically stable orthogonalization method using Newton's iteration (ONI), to learn a layer-wise orthogonal weight matrix in DNNs.

50, TITLE:      A Survey on Conversational Recommender Systems
http://arxiv.org/abs/2004.00646
AUTHORS:        Dietmar Jannach ; Ahtsham Manzoor ; Wanling Cai ; Li Chen
COMMENTS:       35 pages, 5 figures. Submitted for publication
HIGHLIGHT:      Conversational recommender systems (CRS) take a different approach and support a richer set of interactions.

51, TITLE:      Multi-Modal Video Forensic Platform for Investigating Post-Terrorist Attack Scenarios
http://arxiv.org/abs/2004.01023
AUTHORS:        Alexander Schindler ; Andrew Lindley ; Anahid Jalali ; Martin Boyer ; Sergiu Gordea ; Ross King
HIGHLIGHT:      We present a video analytic platform that integrates visual and audio analytic modules and fuses information from surveillance cameras and video uploads from eyewitnesses.

52, TITLE:      Learning Longterm Representations for Person Re-Identification Using Radio Signals
http://arxiv.org/abs/2004.01091
AUTHORS:        Lijie Fan ; Tianhong Li ; Rongyao Fang ; Rumen Hristov ; Yuan Yuan ; Dina Katabi
COMMENTS:       CVPR 2020. The first three authors contributed equally to this paper
HIGHLIGHT:      In this paper, we introduce RF-ReID, a novel approach that harnesses radio frequency (RF) signals for longterm person ReID.

53, TITLE:      Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios
http://arxiv.org/abs/2004.01101
AUTHORS:        Xiaoliang Wang ; Yeqiang Qian ; Chunxiang Wang ; Ming Yang
COMMENTS:       Submitted to IEEE ACCESS
HIGHLIGHT:      To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios.

54, TITLE:      MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model
http://arxiv.org/abs/2004.01095
AUTHORS:        Han Fu ; Rui Wu ; Chenghao Liu ; Jianling Sun
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we focus on the task of cross-modal retrieval between food images and cooking recipes.

55, TITLE:      Effect of Annotation Errors on Drone Detection with YOLOv3
http://arxiv.org/abs/2004.01059
AUTHORS:        Aybora Koksal ; Kutalmis Gokalp Ince ; A. Aydin Alatan
HIGHLIGHT:      In this work, different types of annotation errors for object detection problem are simulated and the performance of a popular state-of-the-art object detector, YOLOv3, with erroneous annotations during training and testing stages is examined.

56, TITLE:      Model-based disentanglement of lens occlusions
http://arxiv.org/abs/2004.01071
AUTHORS:        Fabio Pizzati ; Pietro Cerri ; Raoul de Charette
COMMENTS:       Submitted to conference
HIGHLIGHT:      We propose an unsupervised model-based disentanglement training, which learns to disentangle scene from lens occlusion and can regress the occlusion model parameters from target database.

57, TITLE:      Robots in the Danger Zone: Exploring Public Perception through Engagement
http://arxiv.org/abs/2004.00689
AUTHORS:        David A. Robb ; Muneeb I. Ahmad ; Carlo Tiseo ; Simona Aracri ; Alistair C. McConnell ; Vincent Page ; Christian Dondrup ; Francisco J. Chiyah Garcia ; Hai-Nguyen Nguyen ; Èric Pairet ; Paola Ardón Ramírez ; Tushar Semwal ; Hazel M. Taylor ; Lindsay J. Wilson ; David Lane ; Helen Hastie ; Katrin Lohan
COMMENTS:       Accepted in HRI 2020, Keywords: Human robot interaction, robotics, artificial intelligence, public engagement, public perceptions of robots, robotics and society
HIGHLIGHT:      In this paper, we describe our first iteration of a high throughput in-person public engagement activity.

58, TITLE:      Combating The Machine Ethics Crisis: An Educational Approach
http://arxiv.org/abs/2004.00817
AUTHORS:        Tai Vu
HIGHLIGHT:      In response to such a phenomenon, this study proposes a feasible solution that combines ethics and computer science materials in artificial intelligent classrooms.

59, TITLE:      Learning Representations For Images With Hierarchical Labels
http://arxiv.org/abs/2004.00909
AUTHORS:        Ankit Dhall
COMMENTS:       Master thesis
HIGHLIGHT:      In this thesis we present a set of methods to leverage information about the semantic hierarchy induced by class labels.

60, TITLE:      Exploration of Reinforcement Learning for Event Camera using Car-like Robots
http://arxiv.org/abs/2004.00801
AUTHORS:        Riku Arakawa ; Shintaro Shiba
HIGHLIGHT:      To handle a stream of events for reinforcement learning, we introduced an image-like feature and demonstrated the feasibility of training an agent in a simulator for two tasks: fast collision avoidance and obstacle tracking.

61, TITLE:      Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization
http://arxiv.org/abs/2004.00753
AUTHORS:        Yanwei Zhao ; Ping Yang ; Qiu Guan ; Jianwei Zheng ; Wanliang Wang
COMMENTS:       17 pages, 10 figures, 5 tables
HIGHLIGHT:      By taking both advantages of image domain and transform domain in a general framework, we propose a sparsity transform learning and weighted singular values minimization method (STLWSM) for IDN problems.

62, TITLE:      End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images
http://arxiv.org/abs/2004.00871
AUTHORS:        Yoni Kasten ; Daniel Doktofsky ; Ilya Kovler
HIGHLIGHT:      We present an end-to-end Convolutional Neural Network (CNN) approach for 3D reconstruction of knee bones directly from two bi-planar X-ray images.

63, TITLE:      How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability in Context
http://arxiv.org/abs/2004.00881
AUTHORS:        Jey Han Lau ; Carlos S. Armendariz ; Shalom Lappin ; Matthew Purver ; Chang Shu
COMMENTS:       14 pages. Author's final version, accepted for publication in Transactions of the Association for Computational Linguistics
HIGHLIGHT:      We study the influence of context on sentence acceptability.

64, TITLE:      Revisiting the linearity in cross-lingual embedding mappings: from a perspective of word analogies
http://arxiv.org/abs/2004.01079
AUTHORS:        Xutan Peng ; Chenghua Lin ; Mark Stevenson ; Chen li
COMMENTS:       Comments welcome!
HIGHLIGHT:      In this paper, we rigorously explain that the linearity assumption relies on the consistency of analogical relations encoded by multilingual embeddings.

65, TITLE:      NUBES: A Corpus of Negation and Uncertainty in Spanish Clinical Texts
http://arxiv.org/abs/2004.01092
AUTHORS:        Salvador Lima ; Naiara Perez ; Montse Cuadros ; German Rigau
COMMENTS:       Accepted at the Twelfth International Conference on Language Resources and Evaluation (LREC 2020)
HIGHLIGHT:      This paper introduces the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish).

66, TITLE:      Causal Inference of Script Knowledge
http://arxiv.org/abs/2004.01174
AUTHORS:        Noah Weber ; Rachel Rudinger ; Benjamin Van Durme
COMMENTS:       Pre-Print
HIGHLIGHT:      We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions.

67, TITLE:      Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation
http://arxiv.org/abs/2004.00794
AUTHORS:        Zhonghao Wang ; Yunchao Wei ; Rogerior Feris ; Jinjun Xiong ; Wen-Mei Hwu ; Thomas S. Huang ; Honghui Shi
HIGHLIGHT:      To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-level Shift (ASS), which can successfully promote the distribution consistency from both global and local views.

68, TITLE:      SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience
http://arxiv.org/abs/2004.00797
AUTHORS:        Umar Asif ; Stefan Von Cavallar ; Jianbin Tang ; Stefan Harre
HIGHLIGHT:      In this paper, we explore ways to overcome the above challenges and present Single Shot Human Fall Detector (SSHFD), a deep learning based framework for automatic fall detection from a single image.

69, TITLE:      Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences
http://arxiv.org/abs/2004.00740
AUTHORS:        Huai Yu ; Weikun Zhen ; Wen Yang ; Ji Zhang ; Sebastian Scherer
COMMENTS:       Submitted to IROS 2020
HIGHLIGHT:      To overcome these problems, we propose an efficient monocular camera localization method in prior LiDAR maps using directly estimated 2D-3D line correspondences.

70, TITLE:      Consistent Multiple Sequence Decoding
http://arxiv.org/abs/2004.00760
AUTHORS:        Bicheng Xu ; Leonid Sigal
HIGHLIGHT:      In this paper, we introduce a consistent multiple sequence decoding architecture, which is while relatively simple, is general and allows for consistent and simultaneous decoding of an arbitrary number of sequences.

71, TITLE:      Scene-Adaptive Video Frame Interpolation via Meta-Learning
http://arxiv.org/abs/2004.00779
AUTHORS:        Myungsub Choi ; Janghoon Choi ; Sungyong Baik ; Tae Hyun Kim ; Kyoung Mu Lee
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we propose to adapt the model to each video by making use of additional information that is readily available at test time and yet has not been exploited in previous works.

72, TITLE:      Graph-based fusion for change detection in multi-spectral images
http://arxiv.org/abs/2004.00786
AUTHORS:        David Alejandro Jimenez Sierra ; Hernán Darío Benítez Restrepo ; Hernán Darío Vargas Cardonay ; Jocelyn Chanussot
COMMENTS:       Four pages conference paper, four figures
HIGHLIGHT:      In this paper we address the problem of change detection in multi-spectral images by proposing a data-driven framework of graph-based data fusion.

73, TITLE:      Object-Centric Image Generation with Factored Depths, Locations, and Appearances
http://arxiv.org/abs/2004.00642
AUTHORS:        Titas Anciukevicius ; Christoph H. Lampert ; Paul Henderson
HIGHLIGHT:      We present a generative model of images that explicitly reasons over the set of objects they show.



==========Updates to Previous Papers==========
1, 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.

2, TITLE:       SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images
http://arxiv.org/abs/1810.12959
AUTHORS:        Han Liu ; Lei Wang ; Yandong Nan ; Faguang Jin ; Qi Wang ; Jiantao Pu
COMMENTS:       10 pages, 9 figures
HIGHLIGHT:      To address these issues, we present a novel method termed as segmentation-based deep fusion network (SDFN), which leverages the domain knowledge and the higherresolution information of local lung regions.

3, TITLE:       On adversarial patches: real-world attack on ArcFace-100 face recognition system
http://arxiv.org/abs/1910.07067
AUTHORS:        Mikhail Pautov ; Grigorii Melnikov ; Edgar Kaziakhmedov ; Klim Kireev ; Aleksandr Petiushko
HIGHLIGHT:      In this paper, we study the problem of real-world attacks on face recognition systems.

4, TITLE:       Graph convolutional networks for learning with few clean and many noisy labels
http://arxiv.org/abs/1910.00324
AUTHORS:        Ahmet Iscen ; Giorgos Tolias ; Yannis Avrithis ; Ondrej Chum ; Cordelia Schmid
HIGHLIGHT:      In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given.

5, TITLE:       SGAS: Sequential Greedy Architecture Search
http://arxiv.org/abs/1912.00195
AUTHORS:        Guohao Li ; Guocheng Qian ; Itzel C. Delgadillo ; Matthias Müller ; Ali Thabet ; Bernard Ghanem
COMMENTS:       Accepted at CVPR'2020. Project website: https://www.deepgcns.org/auto/sgas
HIGHLIGHT:      Aiming to alleviate this common issue, we introduce sequential greedy architecture search (SGAS), an efficient method for neural architecture search.

6, TITLE:       Fake News Detection by means of Uncertainty Weighted Causal Graphs
http://arxiv.org/abs/2002.01065
AUTHORS:        Eduardo C. Garrido-Merchán ; Cristina Puente ; Rafael Palacios
HIGHLIGHT:      In this work, we propose a mechanism to detect fake news through a classifier based on weighted causal graphs.

7, TITLE:       Choice functions based on sets of strict partial orders: an axiomatic characterisation
http://arxiv.org/abs/2003.11631
AUTHORS:        Jasper De Bock
HIGHLIGHT:      I here provide a very general axiomatic characterisation for choice functions of this form.

8, TITLE:       Polarity Loss for Zero-shot Object Detection
http://arxiv.org/abs/1811.08982
AUTHORS:        Shafin Rahman ; Salman Khan ; Nick Barnes
HIGHLIGHT:      In this paper, we propose a novel loss function called 'Polarity loss', that promotes correct visual-semantic alignment for an improved zero-shot object detection.

9, TITLE:       Ensemble Knowledge Distillation for Learning Improved and Efficient Networks
http://arxiv.org/abs/1909.08097
AUTHORS:        Umar Asif ; Jianbin Tang ; Stefan Harrer
HIGHLIGHT:      In this paper, we present a framework for learning compact CNN models with improved classification performance and model generalization.

10, TITLE:      Orthogonal Convolutional Neural Networks
http://arxiv.org/abs/1911.12207
AUTHORS:        Jiayun Wang ; Yubei Chen ; Rudrasis Chakraborty ; Stella X. Yu
COMMENTS:       To appear in CVPR 2020, project page http://pwang.pw/ocnn.html
HIGHLIGHT:      We develop an efficient approach to impose filter orthogonality on a convolutional layer based on the doubly block-Toeplitz matrix representation of the convolutional kernel, instead of the common kernel orthogonality approach, which we show is only necessary but not sufficient for ensuring orthogonal convolutions.

11, TITLE:      Prediction and Description of Near-Future Activities in Video
http://arxiv.org/abs/1908.00943
AUTHORS:        Tahmida Mahmud ; Mohammad Billah ; Mahmudul Hasan ; Amit K. Roy-Chowdhury
COMMENTS:       14 pages, 4 figures, 14 tables
HIGHLIGHT:      In this work, we propose a system that can infer the labels and the captions of a sequence of future activities.

12, TITLE:      Analysing the Extent of Misinformation in Cancer Related Tweets
http://arxiv.org/abs/2003.13657
AUTHORS:        Rakesh Bal ; Sayan Sinha ; Swastika Dutta ; Rishabh 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.

13, TITLE:      Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
http://arxiv.org/abs/1911.11834
AUTHORS:        Zeyu Wang ; Klint Qinami ; Ioannis Christos Karakozis ; Kyle Genova ; Prem Nair ; Kenji Hata ; Olga Russakovsky
COMMENTS:       To appear in CVPR 2020
HIGHLIGHT:      We highlight the shortcomings of popular adversarial training approaches for bias mitigation, propose a simple but similarly effective alternative to the inference-time Reducing Bias Amplification method of Zhao et al., and design a domain-independent training technique that outperforms all other methods.

14, TITLE:      Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks
http://arxiv.org/abs/1808.03114
AUTHORS:        Alex Bäuerle ; Heiko Neumann ; Timo Ropinski
HIGHLIGHT:      We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set.

15, TITLE:      CenterMask : Real-Time Anchor-Free Instance Segmentation
http://arxiv.org/abs/1911.06667
AUTHORS:        Youngwan Lee ; Jongyoul Park
COMMENTS:       CVPR 2020
HIGHLIGHT:      We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN.

16, TITLE:      SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
http://arxiv.org/abs/1911.06188
AUTHORS:        Yinda Xu ; Zeyu Wang ; Zuoxin Li ; Ye Yuan ; Gang Yu
COMMENTS:       Accepted by AAAI 2020
HIGHLIGHT:      After a careful analysis, we propose a set of practical guidelines of target state estimation for high-performance generic object tracker design.

17, TITLE:      Automated Design of Deep Learning Methods for Biomedical Image Segmentation
http://arxiv.org/abs/1904.08128
AUTHORS:        Fabian Isensee ; Paul F. Jäger ; Simon A. A. Kohl ; Jens Petersen ; Klaus H. Maier-Hein
COMMENTS:       * Fabian Isensee and Paul F. J\"ager share the first authorship
HIGHLIGHT:      We propose nnU-Net, a deep learning framework that condenses the current domain knowledge and autonomously takes the key decisions required to transfer a basic architecture to different datasets and segmentation tasks.

18, TITLE:      Towards Generalization Across Depth for Monocular 3D Object Detection
http://arxiv.org/abs/1912.08035
AUTHORS:        Andrea Simonelli ; Samuel Rota Bulò ; Lorenzo Porzi ; Elisa Ricci ; Peter Kontschieder
HIGHLIGHT:      In particular, in this work we show that, thanks to our virtual views generation process, a lightweight, single-stage architecture suffices to set new state-of-the-art results on the popular KITTI3D benchmark.

19, TITLE:      Quaternion Product Units for Deep Learning on 3D Rotation Groups
http://arxiv.org/abs/1912.07791
AUTHORS:        Xuan Zhang ; Shaofei Qin ; Yi Xu ; Hongteng Xu
COMMENTS:       CVPR 2020
HIGHLIGHT:      We propose a novel quaternion product unit (QPU) to represent data on 3D rotation groups.

20, TITLE:      Learning to Have an Ear for Face Super-Resolution
http://arxiv.org/abs/1909.12780
AUTHORS:        Givi Meishvili ; Simon Jenni ; Paolo Favaro
HIGHLIGHT:      We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size).

21, 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.

22, TITLE:      Neuroevolution of Self-Interpretable Agents
http://arxiv.org/abs/2003.08165
AUTHORS:        Yujin Tang ; Duong Nguyen ; David Ha
COMMENTS:       To appear at the Genetic and Evolutionary Computation Conference (GECCO 2020) as a full paper
HIGHLIGHT:      Motivated by selective attention, we study the properties of artificial agents that perceive the world through the lens of a self-attention bottleneck.

23, TITLE:      MineGAN: effective knowledge transfer from GANs to target domains with few images
http://arxiv.org/abs/1912.05270
AUTHORS:        Yaxing Wang ; Abel Gonzalez-Garcia ; David Berga ; Luis Herranz ; Fahad Shahbaz Khan ; Joost van de Weijer
COMMENTS:       CVPR2020
HIGHLIGHT:      Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective.

24, TITLE:      Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images
http://arxiv.org/abs/2001.01599
AUTHORS:        Noriaki Hashimoto ; Daisuke Fukushima ; Ryoichi Koga ; Yusuke Takagi ; Kaho Ko ; Kei Kohno ; Masato Nakaguro ; Shigeo Nakamura ; Hidekata Hontani ; Ichiro Takeuchi
COMMENTS:       Accepted to CVPR2020
HIGHLIGHT:      We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI).

25, TITLE:      Semi-supervised Learning for Few-shot Image-to-Image Translation
http://arxiv.org/abs/2003.13853
AUTHORS:        Yaxing Wang ; Salman Khan ; Abel Gonzalez-Garcia ; Joost van de Weijer ; Fahad Shahbaz Khan
COMMENTS:       CVPR2020
HIGHLIGHT:      In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training.

26, TITLE:      Siam R-CNN: Visual Tracking by Re-Detection
http://arxiv.org/abs/1911.12836
AUTHORS:        Paul Voigtlaender ; Jonathon Luiten ; Philip H. S. Torr ; Bastian Leibe
COMMENTS:       CVPR 2020 camera-ready version
HIGHLIGHT:      We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking.

27, TITLE:      Semantically Multi-modal Image Synthesis
http://arxiv.org/abs/2003.12697
AUTHORS:        Zhen Zhu ; Zhiliang Xu ; Ansheng You ; Xiang Bai
COMMENTS:       To appear in CVPR 2020
HIGHLIGHT:      In this paper, we focus on semantically multi-modal image synthesis (SMIS) task, namely, generating multi-modal images at the semantic level.

28, TITLE:      PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes
http://arxiv.org/abs/1911.10949
AUTHORS:        Rundi Wu ; Yixin Zhuang ; Kai Xu ; Hao Zhang ; Baoquan Chen
COMMENTS:       Accepted to CVPR 2020. Code available at https://github.com/ChrisWu1997/PQ-NET
HIGHLIGHT:      We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly.

29, TITLE:      The minimal probabilistic and quantum finite automata recognizing uncountably many languages with fixed cutpoints
http://arxiv.org/abs/1904.01381
AUTHORS:        Aleksejs Naumovs ; Maksims Dimitrijevs ; Abuzer Yakaryılmaz
COMMENTS:       11 pages, minor revisions
HIGHLIGHT:      In this note, we prove the same results for fixed cutpoints: each recognized language is associated with an automaton (i.e., algorithm), and the proofs use the fact that there are uncountably many automata.

30, TITLE:      Life is Random, Time is Not: Markov Decision Processes with Window Objectives
http://arxiv.org/abs/1901.03571
AUTHORS:        Thomas Brihaye ; Florent Delgrange ; Youssouf Oualhadj ; Mickael Randour
COMMENTS:       Full version of CONCUR'19 paper, accepted in LMCS
HIGHLIGHT:      In this work, we extend the window framework to stochastic environments by considering Markov decision processes.

31, TITLE:      Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors
http://arxiv.org/abs/1911.11288
AUTHORS:        Sergey Zakharov ; Wadim Kehl ; Arjun Bhargava ; Adrien Gaidon
COMMENTS:       CVPR 2020 (Oral). 8 pages + supplementary material. The first two authors contributed equally to this work
HIGHLIGHT:      We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data.

32, 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
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we address this issue and present a method to generate universal 3D adversarial objects to fool LiDAR detectors.

33, TITLE:      CaDIS: Cataract Dataset for Image Segmentation
http://arxiv.org/abs/1906.11586
AUTHORS:        Maria Grammatikopoulou ; Evangello Flouty ; Abdolrahim Kadkhodamohammadi ; Gwenol'e Quellec ; Andre Chow ; Jean Nehme ; Imanol Luengo ; Danail Stoyanov
HIGHLIGHT:      This paper introduces a dataset for semantic segmentation of cataract surgery videos.

34, TITLE:      RN-VID: A Feature Fusion Architecture for Video Object Detection
http://arxiv.org/abs/2003.10898
AUTHORS:        Hughes Perreault ; Maguelonne Héritier ; Pierre Gravel ; Guillaume-Alexandre Bilodeau ; Nicolas Saunier
HIGHLIGHT:      It is with this idea in mind that we propose RN-VID (standing for RetinaNet-VIDeo), a novel approach to video object detection.

35, TITLE:      Self-Supervised Learning of Video-Induced Visual Invariances
http://arxiv.org/abs/1912.02783
AUTHORS:        Michael Tschannen ; Josip Djolonga ; Marvin Ritter ; Aravindh Mahendran ; Xiaohua Zhai ; Neil Houlsby ; Sylvain Gelly ; Mario Lucic
COMMENTS:       CVPR 2020
HIGHLIGHT:      We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI).

36, TITLE:      SCARLET-NAS: Bridging the gap between Stability and Scalability in Weight-sharing Neural Architecture Search
http://arxiv.org/abs/1908.06022
AUTHORS:        Xiangxiang Chu ; Bo Zhang ; Jixiang Li ; Qingyuan Li ; Ruijun Xu
COMMENTS:       Make one shot nas scalable
HIGHLIGHT:      In this paper, we discover that skip connections bring about significant feature inconsistency compared with other operations, which potentially degrades the supernet performance.

37, TITLE:      Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system
http://arxiv.org/abs/1903.03349
AUTHORS:        Keelin Murphy ; Shifa Salman Habib ; Syed Mohammad Asad Zaidi ; Saira Khowaja ; Aamir Khan ; Jaime Melendez ; Ernst T. Scholten ; Farhan Amad ; Steven Schalekamp ; Maurits Verhagen ; Rick H. H. M. Philipsen ; Annet Meijers ; Bram van Ginneken
COMMENTS:       Published in Scientific Reports
HIGHLIGHT:      In this work we evaluate the latest version of CAD4TB, a commercial software platform designed for this purpose.

38, TITLE:      We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty
http://arxiv.org/abs/1910.10793
AUTHORS:        Tyler LaBonte ; Carianne Martinez ; Scott A. Roberts
COMMENTS:       Preprint
HIGHLIGHT:      We propose a novel 3D Bayesian convolutional neural network (BCNN), the first deep learning method which generates statistically credible geometric uncertainty maps and scales for application to 3D data.

39, TITLE:      Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
http://arxiv.org/abs/2003.01966
AUTHORS:        Ren Yang ; Fabian Mentzer ; Luc Van Gool ; Radu Timofte
COMMENTS:       Published in CVPR 2020
HIGHLIGHT:      In this paper, we propose a Hierarchical Learned Video Compression (HLVC) method with three hierarchical quality layers and a recurrent enhancement network.

40, TITLE:      Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets
http://arxiv.org/abs/2003.12139
AUTHORS:        Yunpeng Zhao ; Mattia Prosperi ; Tianchen Lyu ; Yi Guo ; Jiang Bian
HIGHLIGHT:      To reduce the burden of the manual annotation, yet maintaining its reliability, we devised a crowdsourcing pipeline combined with active learning strategies.

41, TITLE:      Zero-Shot Cross-Lingual Transfer with Meta Learning
http://arxiv.org/abs/2003.02739
AUTHORS:        Farhad Nooralahzadeh ; Giannis Bekoulis ; Johannes Bjerva ; Isabelle Augenstein
HIGHLIGHT:      In this paper, we consider the setting of training models on multiple different languages at the same time, when English training data, but little or no in-language data is available.

42, 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.

43, TITLE:      Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up
http://arxiv.org/abs/1801.04819
AUTHORS:        Matej Hoffmann ; Rolf Pfeifer
COMMENTS:       22 pages, 3 figures
HIGHLIGHT:      We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition.

44, TITLE:      Audio Summarization with Audio Features and Probability Distribution Divergence
http://arxiv.org/abs/2001.07098
AUTHORS:        Carlos-Emiliano González-Gallardo ; Romain Deveaud ; Eric SanJuan ; Juan-Manuel Torres-Moreno
COMMENTS:       20th International Conference on Computational Linguistics and Intelligent Text Processing
HIGHLIGHT:      In this paper we focus on audio summarization based on audio features and the probability of distribution divergence.

45, 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.

46, TITLE:      High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
http://arxiv.org/abs/2003.08177
AUTHORS:        Guan'an Wang ; Shuo Yang ; Huanyu Liu ; Zhicheng Wang ; Yang Yang ; Shuliang Wang ; Gang Yu ; Erjin Zhou ; Jian Sun
COMMENTS:       accepted by CVPR'20
HIGHLIGHT:      In this paper, we propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment.

47, TITLE:      Episode-based Prototype Generating Network for Zero-Shot Learning
http://arxiv.org/abs/1909.03360
AUTHORS:        Yunlong Yu ; Zhong Ji ; Zhongfei Zhang ; Jungong Han
HIGHLIGHT:      We introduce a simple yet effective episode-based training framework for zero-shot learning (ZSL), where the learning system requires to recognize unseen classes given only the corresponding class semantics.

48, TITLE:      Gradient-based Data Augmentation for Semi-Supervised Learning
http://arxiv.org/abs/2003.12824
AUTHORS:        Hiroshi Kaizuka
COMMENTS:       The lower bound of the inequality (line 2 on page 6 ) changed to fit fact 1 (2). Typos in (9) corrected
HIGHLIGHT:      We propose an SSL method named MixGDA by combining various mixup methods and GDA.