2020.03.06.txt

==========New Papers==========
1, TITLE:       Maximum Clique in Disk-Like Intersection Graphs
http://arxiv.org/abs/2003.02583
AUTHORS:        Édouard Bonnet ; Nicolas Grelier ; Tillmann Miltzow
COMMENTS:       23 pages, 5 figures
HIGHLIGHT:      On the algorithmic side, we extend the polynomial-time algorithm for unit disks [Clark '90, Raghavan and Spinrad '03] to translates of any fixed convex set.

2, TITLE:       Characterizations and approximability of hard counting classes below #P
http://arxiv.org/abs/2003.02524
AUTHORS:        Eleni Bakali ; Aggeliki Chalki ; Aris Pagourtzis
COMMENTS:       18 pages, 5 figures, to be published in the proceedings of TAMC2020
HIGHLIGHT:      An important objective of research in counting complexity is to understand which counting problems are approximable.

3, TITLE:       Towards a Complexity-theoretic Understanding of Restarts in SAT solvers
http://arxiv.org/abs/2003.02323
AUTHORS:        Chunxiao Li ; Noah Fleming ; Marc Vinyals ; Toniann Pitassi ; Vijay Ganesh
HIGHLIGHT:      In this paper, we prove a series of theoretical results that characterize the power of restarts for various models of Boolean SAT solvers.

4, TITLE:       A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection
http://arxiv.org/abs/2003.02349
AUTHORS:        Daniele Bonadiman ; Alessandro Moschitti
HIGHLIGHT:      In this paper, we argue that by exploiting the intrinsic structure of the original rank together with an effective word-relatedness encoder, we can achieve competitive results with respect to the state of the art while retaining high efficiency.

5, TITLE:       Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging
http://arxiv.org/abs/2003.02294
AUTHORS:        Javad Fotouhi ; Giacomo Taylor ; Mathias Unberath ; Alex Johnson ; Sing Chun Lee ; Greg Osgood ; Mehran Armand ; Nassir Navab
HIGHLIGHT:      In this paper, the structurally symmetric nature of the pelvic bone is explored and is used to provide interventional image augmentation for treatment of unilateral fractures in patients with traumatic injuries.

6, TITLE:       Demographic Bias in Biometrics: A Survey on an Emerging Challenge
http://arxiv.org/abs/2003.02488
AUTHORS:        P. Drozdowski ; C. Rathgeb ; A. Dantcheva ; N. Damer ; C. Busch
COMMENTS:       14 pages, 3 figures, 3 tables. Submitted to IEEE Transactions on Technology and Society
HIGHLIGHT:      The main contributions of this article are: (1) an overview of the topic of algorithmic bias in the context of biometrics, (2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation, (3) a discussion of the pertinent technical and social matters, and (4) an outline of the remaining challenges and future work items, both from technological and social points of view.

7, TITLE:       Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems
http://arxiv.org/abs/2003.02301
AUTHORS:        Yi Xie ; Cong Shi ; Zhuohang Li ; Jian Liu ; Yingying Chen ; Bo Yuan
HIGHLIGHT:      In this paper, we propose the first real-time, universal, and robust adversarial attack against the state-of-the-art deep neural network (DNN) based speaker recognition system.

8, 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 little or no data is available for languages other than English.

9, TITLE:       Dynamic Experience Replay
http://arxiv.org/abs/2003.02372
AUTHORS:        Jieliang Luo ; Hui Li
COMMENTS:       10 pages, 5 figures, presented at 2019 Conference on Robot Learning (CoRL)
HIGHLIGHT:      We present a novel technique called Dynamic Experience Replay (DER) that allows Reinforcement Learning (RL) algorithms to use experience replay samples not only from human demonstrations but also successful transitions generated by RL agents during training and therefore improve training efficiency.

10, TITLE:      Fact Check-Worthiness Detection as Positive Unlabelled Learning
http://arxiv.org/abs/2003.02736
AUTHORS:        Dustin Wright ; Isabelle Augenstein
COMMENTS:       11 pages, 2 figures, 6 tables
HIGHLIGHT:      We find that check-worthiness detection is a very challenging task in any domain, because it both hinges upon detecting how factual a sentence is, and how likely a sentence is to be believed without verification.

11, TITLE:      HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference
http://arxiv.org/abs/2003.02756
AUTHORS:        Tianyu Liu ; Xin Zheng ; Baobao Chang ; Zhifang Sui
COMMENTS:       LREC 2020
HIGHLIGHT:      In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias.

12, TITLE:      An Empirical Accuracy Law for Sequential Machine Translation: the Case of Google Translate
http://arxiv.org/abs/2003.02817
AUTHORS:        Lucas Nunes Sequeira ; Bruno Moreschi ; Fabio Gagliardi Cozman ; Bernardo Fontes
COMMENTS:       11 pages, 8 figures (mostly graphs), a few mathematical functions and samples of the experiment
HIGHLIGHT:      We have established, through empirical testing, a law that relates the number of translating hops to translation accuracy in sequential machine translation in Google Translate.

13, TITLE:      A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
http://arxiv.org/abs/2003.02541
AUTHORS:        Jian Liang ; Yunbo Wang ; Dapeng Hu ; Ran He ; Jiashi Feng
HIGHLIGHT:      In this paper, we build on domain adversarial learning and propose a novel domain adaptation method BA$^3$US with two new techniques termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS), respectively.

14, TITLE:      Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning
http://arxiv.org/abs/2003.02546
AUTHORS:        Byungsoo Ko ; Geonmo Gu
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      In this paper, inspired by query expansion and database augmentation, we propose an augmentation method in an embedding space for pair-based metric learning losses, called embedding expansion.

15, TITLE:      GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
http://arxiv.org/abs/2003.02567
AUTHORS:        Lei Kang ; Pau Riba ; Yaxing Wang ; Marçal Rusiñol ; Alicia Fornés ; Mauricio Villegas
HIGHLIGHT:      In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words.

16, TITLE:      Detecting Attended Visual Targets in Video
http://arxiv.org/abs/2003.02501
AUTHORS:        Eunji Chong ; Yongxin Wang ; Nataniel Ruiz ; James M. Rehg
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      Specifically, our goal is to identify where each person in each frame of a video is looking, and correctly handle the out-of-frame case. We introduce a new dataset, VideoAttentionTarget, consisting of fully-annotated video clips containing complex and dynamic patterns of real-world gaze behavior.

17, TITLE:      Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
http://arxiv.org/abs/2003.02484
AUTHORS:        Saehyung Lee ; Hyungyu Lee ; Sungroh Yoon
COMMENTS:       To appear in CVPR 2020
HIGHLIGHT:      In this paper, we identify Adversarial Feature Overfitting (AFO), which may cause poor adversarially robust generalization, and we show that adversarial training can overshoot the optimal point in terms of robust generalization, leading to AFO in our simple Gaussian model.

18, TITLE:      ViCE: Visual Counterfactual Explanations for Machine Learning Models
http://arxiv.org/abs/2003.02428
AUTHORS:        Oscar Gomez ; Steffen Holter ; Jun Yuan ; Enrico Bertini
COMMENTS:       4 pages, 2 figures, ACM IUI 2020
HIGHLIGHT:      In this paper we present an interactive visual analytics tool, ViCE, that generates counterfactual explanations to contextualize and evaluate model decisions.

19, TITLE:      Towards Effective Human-AI Collaboration in GUI-Based Interactive Task Learning Agents
http://arxiv.org/abs/2003.02622
AUTHORS:        Toby Jia-Jun Li ; Jingya Chen ; Tom M. Mitchell ; Brad A. Myers
HIGHLIGHT:      We reflect on our past 5 years of efforts on designing, developing and studying the SUGILITE system, discuss the issues on incorporating recent advances in AI with HCI principles in mixed-initiative interactions and multi-modal interactions, and summarize the lessons we learned.

20, TITLE:      BERT as a Teacher: Contextual Embeddings for Sequence-Level Reward
http://arxiv.org/abs/2003.02738
AUTHORS:        Florian Schmidt ; Thomas Hofmann
HIGHLIGHT:      We cast unconditional generation as a reinforcement learning problem and show that our reward function indeed provides a more effective learning signal than n-gram reward in this challenging setting.

21, TITLE:      Team O2AS at the World Robot Summit 2018: An Approach to Robotic Kitting and Assembly Tasks using General Purpose Grippers and Tools
http://arxiv.org/abs/2003.02427
AUTHORS:        Felix von Drigalski ; Chisato Nakashima ; Yoshiya Shibata ; Yoshinori Konishi ; Joshua C. Triyonoputro ; Kaidi Nie ; Damien Petit ; Toshio Ueshiba ; Ryuichi Takase ; Yukiyasu Domae ; Taku Yoshioka ; Yoshihisa Ijiri ; Ixchel G. Ramirez-Alpizar ; Weiwei Wan ; Kensuke Harada
HIGHLIGHT:      We propose a versatile robotic system for kitting and assembly tasks which uses no jigs or commercial tool changers.

22, TITLE:      Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep
http://arxiv.org/abs/2003.02822
AUTHORS:        Behnood Rasti ; Danfeng Hong ; Renlong Hang ; Pedram Ghamisi ; Xudong Kang ; Jocelyn Chanussot ; Jon Atli Benediktsson
HIGHLIGHT:      In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature extraction and its application on hyperspectral image classification.

23, TITLE:      Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation
http://arxiv.org/abs/2003.02824
AUTHORS:        Min-Hung Chen ; Baopu Li ; Yingze Bao ; Ghassan AlRegib ; Zsolt Kira
COMMENTS:       CVPR 2020 (camera-ready + supplementary). Source code: https://github.com/cmhungsteve/SSTDA
HIGHLIGHT:      To reduce the discrepancy, we propose Self-Supervised Temporal Domain Adaptation (SSTDA), which contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics, achieving better performance than other Domain Adaptation (DA) approaches.

24, TITLE:      Combating noisy labels by agreement: A joint training method with co-regularization
http://arxiv.org/abs/2003.02752
AUTHORS:        Hongxin Wei ; Lei Feng ; Xiangyu Chen ; Bo An
COMMENTS:       Code is available at: https://github.com/hongxin001/JoCoR
HIGHLIGHT:      In this paper, we start from a different perspective and propose a robust learning paradigm called JoCoR, which aims to reduce the diversity of two networks during training.

25, TITLE:      Search Space of Adversarial Perturbations against Image Filters
http://arxiv.org/abs/2003.02750
AUTHORS:        Dang Duy Thang ; Toshihiro Matsui
HIGHLIGHT:      In this study, we focus on investigating the ability to create adversarial patterns in search space against defensive methods that use image filters.

26, TITLE:      The Impact of Hole Geometry on Relative Robustness of In-Painting Networks: An Empirical Study
http://arxiv.org/abs/2003.02314
AUTHORS:        Masood S. Mortazavi ; Ning Yan
HIGHLIGHT:      In this paper, we study the robustness of a given in-painting neural network against variations in hole geometry distributions.

27, TITLE:      Learning View and Target Invariant Visual Servoing for Navigation
http://arxiv.org/abs/2003.02327
AUTHORS:        Yimeng Li ; Jana Kosecka
COMMENTS:       Accepted to ICRA 2020
HIGHLIGHT:      In this paper we propose to learn viewpoint invariant and target invariant visual servoing for local mobile robot navigation; given an initial view and the goal view or an image of a target, we train deep convolutional network controller to reach the desired goal.

28, TITLE:      A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI
http://arxiv.org/abs/2003.02371
AUTHORS:        Jens Behley ; Andres Milioto ; Cyrill Stachniss
HIGHLIGHT:      In this paper, we present an extension of SemanticKITTI, which is a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark, for training and evaluation of laser-based panoptic segmentation. We provide the data and discuss the processing steps needed to enrich a given semantic annotation with temporally consistent instance information, i.e., instance information that supplements the semantic labels and identifies the same instance over sequences of LiDAR point clouds.

29, TITLE:      Creating High Resolution Images with a Latent Adversarial Generator
http://arxiv.org/abs/2003.02365
AUTHORS:        David Berthelot ; Peyman Milanfar ; Ian Goodfellow
HIGHLIGHT:      That is to say, instead of generating an arbitrary image as a sample from the manifold of natural images, we propose to sample images from a particular "subspace" of natural images, directed by a low-resolution image from the same subspace.

30, TITLE:      Towards Fair Cross-Domain Adaptation via Generative Learning
http://arxiv.org/abs/2003.02366
AUTHORS:        Tongxin Wang ; Zhengming Ding ; Wei Shao ; Haixu Tang ; Kun Huang
HIGHLIGHT:      To perform fair cross-domain adaptation and boost the performance on these minority categories, we develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.

31, TITLE:      Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing
http://arxiv.org/abs/2003.02640
AUTHORS:        Carmelo Sferrazza ; Thomas Bi ; Raffaello D'Andrea
COMMENTS:       This work has been submitted to the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) for possible publication. Accompanying video: https://youtu.be/dDTga9PgWS0
HIGHLIGHT:      This paper focuses on both these aspects with regard to a vision-based tactile sensor, which aims to reconstruct the distribution of the three-dimensional contact forces applied on its soft surface.

32, TITLE:      An Incremental Explanation of Inference in Hybrid Bayesian Networks for Increasing Model Trustworthiness and Supporting Clinical Decision Making
http://arxiv.org/abs/2003.02599
AUTHORS:        Evangelia Kyrimi ; Somayyeh Mossadegh ; Nigel Tai ; William Marsh
HIGHLIGHT:      In this paper, we propose an incremental explanation of inference that can be applied to hybrid BNs, i.e. those that contain both discrete and continuous nodes.

33, TITLE:      Cumulant-free closed-form formulas for some common (dis)similarities between densities of an exponential family
http://arxiv.org/abs/2003.02469
AUTHORS:        Frank Nielsen ; Richard Nock
COMMENTS:       27 pages
HIGHLIGHT:      In this work, we report (dis)similarity formulas which bypass the explicit use of the cumulant function and highlight the role of quasi-arithmetic means and their multivariate mean operator extensions.

34, TITLE:      AI outperformed every dermatologist: Improved dermoscopic melanoma diagnosis through customizing batch logic and loss function in an optimized Deep CNN architecture
http://arxiv.org/abs/2003.02597
AUTHORS:        Cong Tri Pham ; Mai Chi Luong ; Dung Van Hoang ; Antoine Doucet
COMMENTS:       21 pages, 8 figures
HIGHLIGHT:      This study proposes a method using deep convolutional neural networks aiming to detect melanoma as a binary classification problem.

35, TITLE:      Self-Supervised Spatio-Temporal Representation Learning Using Variable Playback Speed Prediction
http://arxiv.org/abs/2003.02692
AUTHORS:        Hyeon Cho ; Taehoon Kim ; Hyung Jin Chang ; Wonjun Hwang
COMMENTS:       14 pages, 5 figures
HIGHLIGHT:      We propose a self-supervised learning method by predicting the variable playback speeds of a video.

36, TITLE:      Image Generation from Freehand Scene Sketches
http://arxiv.org/abs/2003.02683
AUTHORS:        Chengying Gao ; Qi Liu ; Qi Xu ; Jianzhuang Liu ; Limin Wang ; Changqing Zou
HIGHLIGHT:      We introduce the first method for automatic image generation from scene-level freehand sketches. We build a large-scale composite dataset called SketchyCOCO to comprehensively evaluate our solution.

37, TITLE:      MarginDistillation: distillation for margin-based softmax
http://arxiv.org/abs/2003.02586
AUTHORS:        David Svitov ; Sergey Alyamkin
HIGHLIGHT:      In this paper, we propose a novel distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets.

38, TITLE:      Path Planning Using Probability Tensor Flows
http://arxiv.org/abs/2003.02774
AUTHORS:        Francesco A. N. Palmieri ; Krishna R. Pattipati ; Giovanni Fioretti ; Giovanni Di Gennaro ; Amedeo Buonanno
COMMENTS:       Submitted for journal publication
HIGHLIGHT:      In this paper, probability propagation is applied to model agent's motion in potentially complex scenarios that include goals and obstacles.

39, TITLE:      Talking-Heads Attention
http://arxiv.org/abs/2003.02436
AUTHORS:        Noam Shazeer ; Zhenzhong Lan ; Youlong Cheng ; Nan Ding ; Le Hou
HIGHLIGHT:      We introduce "talking-heads attention" - a variation on multi-head attention which includes linearprojections across the attention-heads dimension, immediately before and after the softmax operation.While inserting only a small number of additional parameters and a moderate amount of additionalcomputation, talking-heads attention leads to better perplexities on masked language modeling tasks, aswell as better quality when transfer-learning to language comprehension and question answering tasks.

40, TITLE:      Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout
http://arxiv.org/abs/2003.02356
AUTHORS:        Filip Graliński ; Tomasz Stanisławek ; Anna Wróblewska ; Dawid Lipiński ; Agnieszka Kaliska ; Paulina Rosalska ; Bartosz Topolski ; Przemysław Biecek
HIGHLIGHT:      To encourage progress on deeper and more complex information extraction, we present a new task (named Kleister) with two new datasets.

41, TITLE:      RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System
http://arxiv.org/abs/2003.02498
AUTHORS:        Helena H. Lee ; Ke Shu ; Palakorn Achananuparp ; Philips Kokoh Prasetyo ; Yue Liu ; Ee-Peng Lim ; Lav R. Varshney
COMMENTS:       Accepted to WWW 2020. Demo track paper
HIGHLIGHT:      We present RecipeGPT, a novel online recipe generation and evaluation system.

42, TITLE:      Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications
http://arxiv.org/abs/2003.02449
AUTHORS:        Chinthaka Gamanayake ; Lahiru Jayasinghe ; Benny Ng ; Chau Yuen
HIGHLIGHT:      In this paper, a novel greedy approach called cluster pruning has been proposed, which provides a structured way of removing filters in a CNN by considering the importance of filters and the underlying hardware architecture.

43, TITLE:      Drone Based RGBT Vehicle Detection and Counting: A Challenge
http://arxiv.org/abs/2003.02437
AUTHORS:        Pengfei Zhu ; Yiming Sun ; Longyin Wen ; Yu Feng ; Qinghua Hu
HIGHLIGHT:      In this paper we present a large-scale vehicle detection and counting benchmark, named DroneVehicle, aiming at advancing visual analysis tasks on the drone platform.

44, TITLE:      End-to-End Trainable One-Stage Parking Slot Detection Integrating Global and Local Information
http://arxiv.org/abs/2003.02445
AUTHORS:        Jae Kyu Suhr ; Ho Gi Jung
HIGHLIGHT:      This paper proposes an end-to-end trainable one-stage parking slot detection method for around view monitor (AVM) images.

45, TITLE:      Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference
http://arxiv.org/abs/2003.02425
AUTHORS:        Chengxi Li ; Stanley H. Chan ; Yi-Ting Chen
HIGHLIGHT:      We propose a framework based on causal inference for risk object identification, an essential task towards driver-centric risk assessment.

46, TITLE:      Fake Generated Painting Detection via Frequency Analysis
http://arxiv.org/abs/2003.02467
AUTHORS:        Yong Bai ; Yuanfang Guo ; Jinjie Wei ; Lin Lu ; Rui Wang ; Yunhong Wang
COMMENTS:       5 pages, 6 figures
HIGHLIGHT:      Based on our observations, we propose Fake Generated Painting Detection via Frequency Analysis (FGPD-FA) by extracting three types of features in frequency domain. Besides, we also propose a digital fake painting detection database for assessing the proposed method.

47, TITLE:      Event-Based Angular Velocity Regression with Spiking Networks
http://arxiv.org/abs/2003.02790
AUTHORS:        Mathias Gehrig ; Sumit Bam Shrestha ; Daniel Mouritzen ; Davide Scaramuzza
HIGHLIGHT:      To assess the performance of SNNs on this task, we introduce a synthetic event camera dataset generated from real-world panoramic images and show that we can successfully train an SNN to perform angular velocity regression. We propose, for the first time, a temporal regression problem of numerical values given events from an event camera.

48, TITLE:      QED: using Quality-Environment-Diversity to evolve resilient robot swarms
http://arxiv.org/abs/2003.02341
AUTHORS:        David M. Bossens ; Danesh Tarapore
HIGHLIGHT:      This study compares different behavioural descriptors, including two generic descriptors that work on a wide range of tasks, one hand-coded descriptor which fits the domain of interest, and one novel type of descriptor based on environmental diversity, which we call Quality-Environment-Diversity (QED).

49, TITLE:      Adaptive Verifiability-Driven Strategy for Evolutionary Approximation of Arithmetic Circuits
http://arxiv.org/abs/2003.02491
AUTHORS:        Milan Ceska ; Jiri Matyas ; Vojtech Mrazek ; Lukas Sekanina ; Zdenek Vasicek ; Tomas Vojnar
HIGHLIGHT:      We present a novel approach for designing complex approximate arithmetic circuits that trade correctness for power consumption and play important role in many energy-aware applications.

50, TITLE:      Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning
http://arxiv.org/abs/2003.02357
AUTHORS:        Christopher H. Bennett ; T. Patrick Xiao ; Can Cui ; Naimul Hassan ; Otitoaleke G. Akinola ; Jean Anne C. Incorvia ; Alvaro Velasquez ; Joseph S. Friedman ; Matthew J. Marinella
HIGHLIGHT:      We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ).

51, TITLE:      Harnessing Multi-View Perspective of Light Fields for Low-Light Imaging
http://arxiv.org/abs/2003.02438
AUTHORS:        Mohit Lamba ; Kranthi Kumar ; Kaushik Mitra
HIGHLIGHT:      We, therefore, propose a deep neural network for Low-Light Light Field (L3F) restoration, which we refer to as L3Fnet. To facilitate learning-based techniques for low-light LF imaging, we collected a comprehensive LF dataset of various scenes. To further analyze the performance of low-light reconstruction methods, we also propose an L3F-wild dataset that contains LF captured late at night with almost zero lux values.



==========Updates to Previous Papers==========
1, TITLE:       Complexity Analysis of Generalized and Fractional Hypertree Decompositions
http://arxiv.org/abs/2002.05239
AUTHORS:        Georg Gottlob ; Matthias Lanzinger ; Reinhard Pichler ; Igor Razgon
COMMENTS:       This is a significantly extended and enhanced version of arXiv:1611.01090
HIGHLIGHT:      We settle this open problem by showing that checking $fhw(H)\leq k$ is NP-complete, even for $k=2$.

2, TITLE:       Natural Language Processing Advancements By Deep Learning: A Survey
http://arxiv.org/abs/2003.01200
AUTHORS:        Amirsina Torfi ; Rouzbeh A. Shirvani ; Yaser Keneshloo ; Nader Tavvaf ; Edward A. Fox
HIGHLIGHT:      It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas.

3, TITLE:       PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices
http://arxiv.org/abs/1909.05073
AUTHORS:        Xiaolong Ma ; Fu-Ming Guo ; Wei Niu ; Xue Lin ; Jian Tang ; Kaisheng Ma ; Bin Ren ; Yanzhi Wang
COMMENTS:       To appear in Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20)
HIGHLIGHT:      In this paper, we introduce PCONV, comprising a new sparsity dimension, -- fine-grained pruning patterns inside the coarse-grained structures.

4, TITLE:       Randomized Smoothing of All Shapes and Sizes
http://arxiv.org/abs/2002.08118
AUTHORS:        Greg Yang ; Tony Duan ; J. Edward Hu ; Hadi Salman ; Ilya Razenshteyn ; Jerry Li
COMMENTS:       9 pages main text, 47 pages total
HIGHLIGHT:      In this work we propose a novel framework for devising and analyzing randomized smoothing schemes, and validate its effectiveness in practice.

5, TITLE:       MMA Training: Direct Input Space Margin Maximization through Adversarial Training
http://arxiv.org/abs/1812.02637
AUTHORS:        Gavin Weiguang Ding ; Yash Sharma ; Kry Yik Chau Lui ; Ruitong Huang
COMMENTS:       Published at the Eighth International Conference on Learning Representations (ICLR 2020), https://openreview.net/forum?id=HkeryxBtPB
HIGHLIGHT:      We propose Max-Margin Adversarial (MMA) training to directly maximize the margins to achieve adversarial robustness.

6, TITLE:       Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
http://arxiv.org/abs/1911.09298
AUTHORS:        Ligong Han ; Ruijiang Gao ; Mun Kim ; Xin Tao ; Bo Liu ; Dimitris Metaxas
COMMENTS:       Accepted for spotlight at AAAI-20
HIGHLIGHT:      To address this problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing.

7, TITLE:       ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks
http://arxiv.org/abs/1905.11173
AUTHORS:        Xiaoqi Jia ; Jianwei Tai ; Hang Zhou ; Yakai Li ; Weijuan Zhang ; Haichao Du ; Qingjia Huang
COMMENTS:       Accepted by ECAI 2020, 8 pages, 4 figures
HIGHLIGHT:      Besides, we introduce an approach for migrating emotion information across different languages by using transfer learning.

8, TITLE:       Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading from Plain Radiographs
http://arxiv.org/abs/2003.01944
AUTHORS:        Huy Hoang Nguyen ; Simo Saarakkala ; Matthew Blaschko ; Aleksei Tiulpin
COMMENTS:       10 pages, 2 supplementary pages, manuscript under review
HIGHLIGHT:      In this study, we propose the Semixup algorithm, a semi-supervised learning (SSL) approach to leverage unlabeled data.

9, TITLE:       Automatic Target Detection for Sparse Hyperspectral Images
http://arxiv.org/abs/1904.09030
AUTHORS:        Ahmad W. Bitar ; Jean-Philippe Ovarlez ; Loong-Fah Cheong ; Ali Chehab
COMMENTS:       Accepted for publication in the book "Hyperspectral Image Analysis - Advances in Signal Processing and Machine Learning". arXiv admin note: text overlap with arXiv:1711.08970, arXiv:1808.06490
HIGHLIGHT:      In this work, a novel target detector for hyperspectral imagery is developed.

10, TITLE:      Spectral Characterization of functional MRI data on voxel-resolution cortical graphs
http://arxiv.org/abs/1910.09507
AUTHORS:        Hamid Behjat ; Martin Larsson
COMMENTS:       Updated Fig. 4(a) using data from 100 subjects. Fixed an error in Fig. 4(c) w.r.t. ordering of subjects based on graph size. Minor edits across the text
HIGHLIGHT:      We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D.

11, TITLE:      Temporal Constraint Satisfaction Problems in Fixed-Point Logic
http://arxiv.org/abs/2002.09451
AUTHORS:        Manuel Bodirsky ; Wied Pakusa ; Jakub Rydval
COMMENTS:       59 pages
HIGHLIGHT:      We prove that there is no Maltsev condition that characterises Datalog already for the CSPs of first-order reducts of (Q;<); such CSPs are called temporal CSPs and are of fundamental importance in infinite-domain constraint satisfaction.

12, TITLE:      Outbound Translation User Interface Ptakopet: A Pilot Study
http://arxiv.org/abs/1911.10835
AUTHORS:        Vilém Zouhar ; Ondřej Bojar
HIGHLIGHT:      We call the task "outbound translation" and explore it by introducing an open-source modular system Ptakop\v{e}t. Its main purpose is to inspect human interaction with MT systems enhanced with additional subsystems, such as backward translation and quality estimation.

13, TITLE:      Nested Reasoning About Autonomous Agents Using Probabilistic Programs
http://arxiv.org/abs/1812.01569
AUTHORS:        Iris Rubi Seaman ; Jan-Willem van de Meent ; David Wingate
HIGHLIGHT:      We develop a planning-as-inference framework in which agents perform nested simulation to reason about the behavior of other agents in an online manner.

14, TITLE:      Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift
http://arxiv.org/abs/1912.01072
AUTHORS:        Matej Martinc ; Petra Kralj Novak ; Senja Pollak
COMMENTS:       Accepted to Language Resources and Evaluation (LREC 2020)
HIGHLIGHT:      We propose a new method that leverages contextual embeddings for the task of diachronic semantic shift detection by generating time specific word representations from BERT embeddings.

15, TITLE:      Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
http://arxiv.org/abs/2003.00330
AUTHORS:        Luis Lamb ; Artur Garcez ; Marco Gori ; Marcelo Prates ; Pedro Avelar ; Moshe Vardi
HIGHLIGHT:      In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing.

16, TITLE:      Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose
http://arxiv.org/abs/2002.10137
AUTHORS:        Ran Yi ; Zipeng Ye ; Juyong Zhang ; Hujun Bao ; Yong-Jin Liu
COMMENTS:       12 pages, 9 figures
HIGHLIGHT:      In this paper, we address this problem by proposing a deep neural network model that takes an audio signal A of a source person and a very short video V of a target person as input, and outputs a synthesized high-quality talking face video with personalized head pose (making use of the visual information in V), expression and lip synchronization (by considering both A and V).

17, TITLE:      PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection
http://arxiv.org/abs/1912.12898
AUTHORS:        Yue Liao ; Si Liu ; Fei Wang ; Yanjie Chen ; Chen Qian ; Jiashi Feng
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We propose a single-stage Human-Object Interaction (HOI) detection method that has outperformed all existing methods on HICO-DET dataset at 37 fps on a single Titan XP GPU. Additionally, we build a new applicationoriented database named HOI-A, which severs as a good supplement to the existing datasets.

18, TITLE:      ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
http://arxiv.org/abs/2001.11394
AUTHORS:        Lichao Mou ; Yuansheng Hua ; Pu Jin ; Xiao Xiang Zhu
HIGHLIGHT:      In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds.

19, TITLE:      Benchmarking the Robustness of Semantic Segmentation Models
http://arxiv.org/abs/1908.05005
AUTHORS:        Christoph Kamann ; Carsten Rother
HIGHLIGHT:      To increase the realism of our study, we utilize almost 400,000 images generated from PASCAL VOC 2012, Cityscapes, and ADE20K.

20, TITLE:      Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness
http://arxiv.org/abs/1906.08482
AUTHORS:        Antônio H. Ribeiro ; Koen Tiels ; Luis A. Aguirre ; Thomas B. Schön
COMMENTS:       To appear in the Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. PMLR: Volume 108. This paper was previously titled "The trade-off between long-term memory and smoothness for recurrent networks". The current version subsumes all previous versions
HIGHLIGHT:      In this paper, we argue that this principle, while powerful, might need some refinement to explain recent developments.

21, TITLE:      Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning
http://arxiv.org/abs/1909.07299
AUTHORS:        Alper Kamil Bozkurt ; Yu Wang ; Michael M. Zavlanos ; Miroslav Pajic
HIGHLIGHT:      We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP).

22, TITLE:      Detecting mixed-unitary quantum channels is NP-hard
http://arxiv.org/abs/1902.03164
AUTHORS:        Colin Do-Yan Lee ; John Watrous
COMMENTS:       24 pages
HIGHLIGHT:      We prove that, given the Choi representation of a quantum channel, it is NP-hard with respect to polynomial-time Turing reductions to determine whether or not that channel is a mixed-unitary channel.

23, TITLE:      Diverse Video Captioning Through Latent Variable Expansion with Conditional GAN
http://arxiv.org/abs/1910.12019
AUTHORS:        Huanhou Xiao ; Jinglun Shi
HIGHLIGHT:      In this paper, we aim to caption each video with multiple descriptions and propose a novel framework.

24, TITLE:      MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation
http://arxiv.org/abs/1810.08534
AUTHORS:        Wei Tang ; Gui Li ; Xinyuan Bao ; Teng Li
HIGHLIGHT:      In this paper, we propose a novel Multi-scale Conditional Generative Adversarial Networks (MsCGAN), aiming to convert the input conditional person image to a synthetic image of any given target pose, whose appearance and the texture are consistent with the input image.

25, TITLE:      You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
http://arxiv.org/abs/1911.06644
AUTHORS:        Okan Köpüklü ; Xiangyu Wei ; Gerhard Rigoll
HIGHLIGHT:      In this work, we present YOWO, a unified CNN architecture for real-time spatiotemporal action localization in video streams.

26, TITLE:      DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network
http://arxiv.org/abs/1910.11088
AUTHORS:        Wei Wang ; Muhamad Risqi U. Saputra ; Peijun Zhao ; Pedro Gusmao ; Bo Yang ; Changhao Chen ; Andrew Markham ; Niki Trigoni
COMMENTS:       To appear in IROS 2019
HIGHLIGHT:      In this work, we investigate how to exploit deep learning to estimate point cloud odometry (PCO), which may serve as a critical component in point cloud-based downstream tasks or learning-based systems.

27, TITLE:      Scientific Image Tampering Detection Based On Noise Inconsistencies: A Method And Datasets
http://arxiv.org/abs/2001.07799
AUTHORS:        Ziyue Xiang ; Daniel E. Acuna
HIGHLIGHT:      Therefore, we propose a scientific-image specific tampering detection method based on noise inconsistencies, which is capable of learning and generalizing to different fields of science.

28, TITLE:      ImagineNet: Restyling Apps Using Neural Style Transfer
http://arxiv.org/abs/2001.04932
AUTHORS:        Michael H. Fischer ; Richard R. Yang ; Monica S. Lam
HIGHLIGHT:      We propose a neural solution by adding a new loss term to the original formulation, which minimizes the squared error in the uncentered cross-covariance of features from different levels in a CNN between the style and output images.

29, TITLE:      Fashion Outfit Complementary Item Retrieval
http://arxiv.org/abs/1912.08967
AUTHORS:        Yen-Liang Lin ; Son Tran ; Larry S. Davis
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      We propose a new framework for outfit complementary item retrieval.

30, TITLE:      Counting Immutable Beans: Reference Counting Optimized for Purely Functional Programming
http://arxiv.org/abs/1908.05647
AUTHORS:        Sebastian Ullrich ; Leonardo de Moura
HIGHLIGHT:      We describe an approach for minimizing the number of reference counts updates using borrowed references and a heuristic for automatically inferring borrow annotations.

31, TITLE:      HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
http://arxiv.org/abs/1905.10687
AUTHORS:        Jakob Kruse ; Gianluca Detommaso ; Robert Scheichl ; Ullrich Köthe
HIGHLIGHT:      This work presents a simple remedy by noting that (affine) coupling can be repeated recursively within the resulting sub-spaces, leading to an efficiently invertible block with dense triangular Jacobian.

32, TITLE:      A complexity chasm for solving sparse polynomial equations over $p$-adic fields
http://arxiv.org/abs/2003.00314
AUTHORS:        J. Maurice Rojas ; Yuyu Zhu
HIGHLIGHT:      We reveal a complexity chasm, separating the trinomial and tetranomial cases, for solving univariate sparse polynomial equations over certain local fields.

33, TITLE:      Knowledge Graphs on the Web -- an Overview
http://arxiv.org/abs/2003.00719
AUTHORS:        Nicolas Heist ; Sven Hertling ; Daniel Ringler ; Heiko Paulheim
COMMENTS:       Chapter of the book "I. Tiddi, F. Lecue, P. Hitzler: Knowledge Graphs for eXplainable AI" (working title), IOS Press [extended version]
HIGHLIGHT:      In this chapter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap.

34, TITLE:      Towards Visually Explaining Variational Autoencoders
http://arxiv.org/abs/1911.07389
AUTHORS:        Wenqian Liu ; Runze Li ; Meng Zheng ; Srikrishna Karanam ; Ziyan Wu ; Bir Bhanu ; Richard J. Radke ; Octavia Camps
COMMENTS:       10 pages, 8 figures, 3 tables, CVPR 2020
HIGHLIGHT:      In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention.

35, TITLE:      The GAN that Warped: Semantic Attribute Editing with Unpaired Data
http://arxiv.org/abs/1811.12784
AUTHORS:        Garoe Dorta ; Sara Vicente ; Neill D. F. Campbell ; Ivor J. A. Simpson
COMMENTS:       CVPR 2020
HIGHLIGHT:      This work proposes to learn how to perform semantic image edits through the application of smooth warp fields.

36, TITLE:      Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval
http://arxiv.org/abs/2002.10310
AUTHORS:        Ayan Kumar Bhunia ; Yongxin Yang ; Timothy M. Hospedales ; Tao Xiang ; Yi-Zhe Song
COMMENTS:       IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020
HIGHLIGHT:      In this paper, we reformulate the conventional FG-SBIR framework to tackle these challenges, with the ultimate goal of retrieving the target photo with the least number of strokes possible.

37, TITLE:      Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
http://arxiv.org/abs/1812.02019
AUTHORS:        Ken Chen ; Fei Chen ; Baisheng Lai ; Zhongming Jin ; Yong Liu ; Kai Li ; Long Wei ; Pengfei Wang ; Yandong Tang ; Jianqiang Huang ; Xian-Sheng Hua
HIGHLIGHT:      In this paper, we present dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive features to represent spatio-temporal structures and predict future traffic flows from surveillance video data.

38, TITLE:      Applying Tensor Decomposition to image for Robustness against Adversarial Attack
http://arxiv.org/abs/2002.12913
AUTHORS:        Seungju Cho ; Tae Joon Jun ; Mingu Kang ; Daeyoung Kim
HIGHLIGHT:      In this paper, we suggest combining tensor decomposition for defending the model against adversarial example.

39, TITLE:      Learning Near Optimal Policies with Low Inherent Bellman Error
http://arxiv.org/abs/2003.00153
AUTHORS:        Andrea Zanette ; Alessandro Lazaric ; Mykel Kochenderfer ; Emma Brunskill
COMMENTS:       Minor fix
HIGHLIGHT:      We study the exploration problem with approximate linear action-value functions in episodic reinforcement learning under the notion of low inherent Bellman error, a condition normally employed to show convergence of approximate value iteration.

40, TITLE:      Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
http://arxiv.org/abs/1911.00361
AUTHORS:        Xishan Zhang ; Shaoli Liu ; Rui Zhang ; Chang Liu ; Di Huang ; Shiyi Zhou ; Jiaming Guo ; Yu Kang ; Qi Guo ; Zidong Du ; Yunji Chen
COMMENTS:       We would like to withdraw the manuscript because it lacks of comparisons. The main contribution is not well verified by experiments
HIGHLIGHT:      Recent emerged quantization technique has been applied to inference of deep neural networks for fast and efficient execution.

41, TITLE:      Reflective-AR Display: An Interaction Methodology for Virtual-Real Alignment in Medical Robotics
http://arxiv.org/abs/1907.10138
AUTHORS:        Javad Fotouhi ; Tianyu Song ; Arian Mehrfard ; Giacomo Taylor ; Qiaochu Wang ; Fengfang Xian ; Alejandro Martin-Gomez ; Bernhard Fuerst ; Mehran Armand ; Mathias Unberath ; Nassir Navab
HIGHLIGHT:      We propose a head-mounted display (HMD) based augmented reality (AR) system designed to guide optimal surgical arm set up.

42, TITLE:      CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association
http://arxiv.org/abs/1902.02256
AUTHORS:        Kaveh Fathian ; Kasra Khosoussi ; Yulun Tian ; Parker Lusk ; Jonathan P. How
HIGHLIGHT:      This work presents the CLEAR (Consistent Lifting, Embedding, and Alignment Rectification) algorithm to address these issues.

43, TITLE:      Graph-Structured Visual Imitation
http://arxiv.org/abs/1907.05518
AUTHORS:        Maximilian Sieb ; Zhou Xian ; Audrey Huang ; Oliver Kroemer ; Katerina Fragkiadaki
COMMENTS:       8 pages, 3 figures, 1 table
HIGHLIGHT:      We cast visual imitation as a visual correspondence problem.

44, TITLE:      Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
http://arxiv.org/abs/2002.00748
AUTHORS:        Bang Liu ; Haojie Wei ; Di Niu ; Haolan Chen ; Yancheng He
COMMENTS:       Accepted by The Web Conference 2020 (WWW 2020) as full paper (oral presentation)
HIGHLIGHT:      In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions.

45, TITLE:      CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
http://arxiv.org/abs/2003.01355
AUTHORS:        Liang Xu ; Xuanwei Zhang ; Qianqian Dong
COMMENTS:       8 pages, 9 tables
HIGHLIGHT:      In this paper, we introduce the Chinese corpus from CLUE organization, CLUECorpus2020, a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation. To facilitate future work on self-supervised learning on Chinese, we release our dataset, new vocabulary, codes, and pre-trained models on Github.

46, TITLE:      A Survey on Natural Language Processing for Fake News Detection
http://arxiv.org/abs/1811.00770
AUTHORS:        Ray Oshikawa ; Jing Qian ; William Yang Wang
COMMENTS:       11 pages, no figure, Accepted to LREC 2020
HIGHLIGHT:      Based on our insights, we outline promising research directions, including more fine-grained, detailed, fair, and practical detection models.

47, TITLE:      Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders
http://arxiv.org/abs/1711.07632
AUTHORS:        Xiaopeng Yang ; Xiaowen Lin ; Shunda Suo ; Ming Li
COMMENTS:       Accepted by IJCAI-18
HIGHLIGHT:      We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables.

48, TITLE:      Temporal Convolutional Attention-based Network For Sequence Modeling
http://arxiv.org/abs/2002.12530
AUTHORS:        Hongyan Hao ; Yan Wang ; Yudi Xia ; Jian Zhao ; Furao Shen
COMMENTS:       7 pages, 4 figures
HIGHLIGHT:      Many powerful feed-forward models based on convolutional networks and attention mechanism were proposed and show more potential to handle sequence modeling tasks.

49, TITLE:      Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models
http://arxiv.org/abs/1911.09762
AUTHORS:        Zhiyun Lu ; Liangliang Cao ; Yu Zhang ; Chung-Cheng Chiu ; James Fan
HIGHLIGHT:      In this paper, we propose to use pre-trained features from end-to-end ASR models to solve speech sentiment analysis as a down-stream task.

50, TITLE:      Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition
http://arxiv.org/abs/2003.01478
AUTHORS:        Jingye Li ; Meishan Zhang ; Donghong Ji ; Yijiang Liu
HIGHLIGHT:      In this paper, we exploit speaker identification (SI) as an auxiliary task to enhance the utterance representation in conversations.

51, TITLE:      Plugin Networks for Inference under Partial Evidence
http://arxiv.org/abs/1901.00326
AUTHORS:        Michal Koperski ; Tomasz Konopczynski ; Rafał Nowak ; Piotr Semberecki ; Tomasz Trzcinski
COMMENTS:       Accepted to WACV 2020
HIGHLIGHT:      In this paper, we propose a novel method to incorporate partial evidence in the inference of deep convolutional neural networks.

52, TITLE:      3D-RelNet: Joint Object and Relational Network for 3D Prediction
http://arxiv.org/abs/1906.02729
AUTHORS:        Nilesh Kulkarni ; Ishan Misra ; Shubham Tulsiani ; Abhinav Gupta
COMMENTS:       Project page: https://nileshkulkarni.github.io/relative3d/
HIGHLIGHT:      We propose an approach to predict the 3D shape and pose for the objects present in a scene.

53, TITLE:      Affective Expression Analysis in-the-wild using Multi-Task Temporal Statistical Deep Learning Model
http://arxiv.org/abs/2002.09120
AUTHORS:        Nhu-Tai Do ; Tram-Tran Nguyen-Quynh ; Soo-Hyung Kim
HIGHLIGHT:      In this paper, we present an affective expression analysis model that deals with the above challenges.

54, TITLE:      Tensor Sparse PCA and Face Recognition: A Novel Approach
http://arxiv.org/abs/1904.08496
AUTHORS:        Loc Hoang Tran ; Linh Hoang Tran
COMMENTS:       It has some errors in the experimental section
HIGHLIGHT:      In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset.

55, TITLE:      EdgeFool: An Adversarial Image Enhancement Filter
http://arxiv.org/abs/1910.12227
AUTHORS:        Ali Shahin Shamsabadi ; Changjae Oh ; Andrea Cavallaro
HIGHLIGHT:      In this paper, we propose EdgeFool, an adversarial image enhancement filter that learns structure-aware adversarial perturbations.

56, TITLE:      S4NN: temporal backpropagation for spiking neural networks with one spike per neuron
http://arxiv.org/abs/1910.09495
AUTHORS:        Saeed Reza Kheradpisheh ; Timothée Masquelier
HIGHLIGHT:      We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding.

57, TITLE:      CNN-based Driver Drowsiness Detection
http://arxiv.org/abs/2001.05137
AUTHORS:        Maryam Hashemi ; Alireza Mirrashid ; Aliasghar Beheshti Shirazi
HIGHLIGHT:      This paper presents a novel system for the problem of driver drowsiness detection.