2021.04.29.txt

1, TITLE: An Optical Neural Network Using Less Than 1 Photon Per Multiplication
AUTHORS: TIANYU WANG et. al.
CATEGORY: physics.optics [physics.optics, cs.ET, cs.LG, cs.NE]
HIGHLIGHT: Using commercially available optical components and standard neural-network training methods, we demonstrated that optical neural networks can operate near the standard quantum limit with extremely low optical powers and still achieve high accuracy.

2, TITLE: Learning from ?ukasiewicz and Meredith: Investigations Into Proof Structures (Extended Version)
AUTHORS: Christoph Wernhard ; Wolfgang Bibel
CATEGORY: cs.AI [cs.AI, cs.LO]
HIGHLIGHT: The material presented in this paper contributes to establishing a basis deemed essential for substantial progress in Automated Deduction.

3, TITLE: Symbolic Abstractions From Data: A PAC Learning Approach
AUTHORS: Alex Devonport ; Adnane Saoud ; Murat Arcak
CATEGORY: cs.AI [cs.AI, cs.LG, cs.SY, eess.SY]
HIGHLIGHT: Symbolic control techniques aim to satisfy complex logic specifications.

4, TITLE: Rule-based Shielding for Partially Observable Monte-Carlo Planning
AUTHORS: Giulio Mazzi ; Alberto Castellini ; Alessandro Farinelli
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In this work, we propose two contributions.

5, TITLE: The Role of General Intelligence in Mathematical Reasoning
AUTHORS: Aviv Keren
CATEGORY: cs.AI [cs.AI, cs.LG, math.HO]
HIGHLIGHT: In this paper, I put the issue into its philosophical and cognitive context.

6, TITLE: SELF & FEIL: Emotion and Intensity Lexicons for Finnish
AUTHORS: Emily Öhman
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: This paper introduces a Sentiment and Emotion Lexicon for Finnish (SELF) and a Finnish Emotion Intensity Lexicon (FEIL).

7, TITLE: MelBERT: Metaphor Detection Via Contextualized Late Interaction Using Metaphorical Identification Theories
AUTHORS: MINJIN CHOI et. al.
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT).

8, TITLE: Semi-supervised Interactive Intent Labeling
AUTHORS: Saurav Sahay ; Eda Okur ; Nagib Hakim ; Lama Nachman
CATEGORY: cs.CL [cs.CL, cs.HC]
HIGHLIGHT: In this work, we have developed an Intent Bulk Labeling system for SDS developers.

9, TITLE: Evaluating Document Representations for Content-based Legal Literature Recommendations
AUTHORS: MALTE OSTENDORFF et. al.
CATEGORY: cs.CL [cs.CL, cs.IR]
HIGHLIGHT: Besides the overall performance, we analyze the methods depending on document length, citation count, and the coverage of their recommendations. We make our source code, models, and datasets publicly available at https://github.com/malteos/legal-document-similarity/.

10, TITLE: Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction
AUTHORS: Peng Su ; Yifan Peng ; K. Vijay-Shanker
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction.

11, TITLE: Multi-view Inference for Relation Extraction with Uncertain Knowledge
AUTHORS: Bo Li ; Wei Ye ; Canming Huang ; Shikun Zhang
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: This paper proposes to exploit uncertain knowledge to improve relation extraction.

12, TITLE: Removing Word-Level Spurious Alignment Between Images and Pseudo-Captions in Unsupervised Image Captioning
AUTHORS: Ukyo Honda ; Yoshitaka Ushiku ; Atsushi Hashimoto ; Taro Watanabe ; Yuji Matsumoto
CATEGORY: cs.CL [cs.CL, cs.CV]
HIGHLIGHT: In this work, we investigate the effect of removing mismatched words from image-sentence alignment to determine how they make this task difficult.

13, TITLE: PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols
AUTHORS: Songlin Yang ; Yanpeng Zhao ; Kewei Tu
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols.

14, TITLE: Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
AUTHORS: HAN-CHIN SHING et. al.
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: This paper introduces the task of generating discharge summaries for a clinical encounter.

15, TITLE: AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking
AUTHORS: Tariq Alhindi ; Amal Alabdulkarim ; Ali Alshehri ; Muhammad Abdul-Mageed ; Preslav Nakov
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: To this end, we present our new Arabic Stance Detection dataset (AraStance) of 910 claims from a diverse set of sources comprising three fact-checking websites and one news website.

16, TITLE: Named Entity Recognition and Linking Augmented with Large-Scale Structured Data
AUTHORS: Pawe? Rychlikowski ; Bart?omiej Najdecki ; Adrian ?a?cucki ; Adam Kaczmarek
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper we describe our submissions to the 2nd and 3rd SlavNER Shared Tasks held at BSNLP 2019 and BSNLP 2021, respectively.

17, TITLE: Kernelization, Proof Complexity and Social Choice
AUTHORS: Gabriel Istrate ; Cosmin Bonchis ; Adrian Craciun
CATEGORY: cs.CC [cs.CC, cs.DS, cs.GT, cs.LO, math.LO]
HIGHLIGHT: We display an application of the notions of kernelization and data reduction from parameterized complexity to proof complexity: Specifically, we show that the existence of data reduction rules for a parameterized problem having (a).

18, TITLE: Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks
AUTHORS: Mohammad Mahfujur Rahman ; Clinton Fookes ; Sridha Sridharan
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In this paper, we propose an end-to-end novel semantic consistent generative adversarial network (SCGAN).

19, TITLE: Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry
AUTHORS: Oussema Bouafif ; Bogdan Khomutenko ; Mohamed Daoudi
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques.

20, TITLE: The Algonauts Project 2021 Challenge: How The Human Brain Makes Sense of A World in Motion
AUTHORS: R. M. CICHY et. al.
CATEGORY: cs.CV [cs.CV, q-bio.NC]
HIGHLIGHT: We provide whole-brain fMRI responses recorded while 10 human participants viewed a rich set of over 1,000 short video clips depicting everyday events.

21, TITLE: PANDA : Perceptually Aware Neural Detection of Anomalies
AUTHORS: Jack W. Barker ; Toby P. Breckon
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we propose a novel fine-grained VAE-GAN architecture trained in a semi-supervised manner in order to detect both visually distinct and subtle anomalies.

22, TITLE: Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings
AUTHORS: INNFARN YOO et. al.
CATEGORY: cs.CV [cs.CV, cs.CR, cs.LG, eess.IV]
HIGHLIGHT: We introduce a novel end-to-end learning framework to solve this problem through: 1) an encoder to covertly embed messages in both mesh geometry and textures; 2) a differentiable renderer to render watermarked 3D objects from different camera angles and under varied lighting conditions; 3) a decoder to recover the messages from 2D rendered images.

23, TITLE: Estimating Egocentric 3D Human Pose in Global Space
AUTHORS: Jian Wang ; Lingjie Liu ; Weipeng Xu ; Kripasindhu Sarkar ; Christian Theobalt
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To tackle these limitations, we present a new method for egocentric global 3D body pose estimation using a single head-mounted fisheye camera.

24, TITLE: SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Shape Alignment
AUTHORS: Elvis Nunez ; Andrew Lizarraga ; Shantanu H. Joshi
CATEGORY: cs.CV [cs.CV, cs.LG, math.DG]
HIGHLIGHT: We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates.

25, TITLE: Classification and Comparison of License Plates Localization Algorithms
AUTHORS: Mustapha Saidallah ; Fatimazahra Taki ; Abdelbaki El Belrhiti El Alaoui ; Abdeslam El Fergougui
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: This paper presents a classification and comparison of License Plates Localization (LPL) algorithms and describes the advantages, disadvantages and improvements made by each of them

26, TITLE: Towards Fair Federated Learning with Zero-Shot Data Augmentation
AUTHORS: WEITUO HAO et. al.
CATEGORY: cs.CV [cs.CV, cs.LG, stat.ML]
HIGHLIGHT: In this work, we aim to provide federated learning schemes with improved fairness.

27, TITLE: HOTR: End-to-End Human-Object Interaction Detection with Transformers
AUTHORS: Bumsoo Kim ; Junhyun Lee ; Jaewoo Kang ; Eun-Sol Kim ; Hyunwoo J. Kim
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we present a novel framework, referred to by HOTR, which directly predicts a set of triplets from an image based on a transformer encoder-decoder architecture.

28, TITLE: Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from A Class-wise Memory Bank
AUTHORS: Inigo Alonso ; Alberto Sabater ; David Ferstl ; Luis Montesano ; Ana C. Murillo
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This work presents a novel approach for semi-supervised semantic segmentation, i.e., per-pixel classification problem assuming that only a small set of the available data is labeled.

29, TITLE: Contrastive Spatial Reasoning on Multi-View Line Drawings
AUTHORS: Siyuan Xiang ; Anbang Yang ; Yanfei Xue ; Yaoqing Yang ; Chen Feng
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: Guided by the hindsight from these experiment results, we propose a simple contrastive learning approach along with other network modifications to improve the baseline performance.

30, TITLE: Incident Detection on Junctions Using Image Processing
AUTHORS: Murat Tulgaç ; Enes Yüncü ; Mohamad-Alhaddad ; Ceylan Yozgatl?gil
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this study, an anomaly detection framework for road junctions is proposed.

31, TITLE: D-OccNet: Detailed 3D Reconstruction Using Cross-Domain Learning
AUTHORS: Minhaj Uddin Ansari ; Talha Bilal ; Naeem Akhter
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this study, we extend the work on Occupancy Networks by exploiting cross-domain learning of image and point cloud domains.

32, TITLE: AdvHaze: Adversarial Haze Attack
AUTHORS: Ruijun Gao ; Qing Guo ; Felix Juefei-Xu ; Hongkai Yu ; Wei Feng
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we instead introduce a novel adversarial attack method based on haze, which is a common phenomenon in real-world scenery.

33, TITLE: On The Unreasonable Effectiveness of Centroids in Image Retrieval
AUTHORS: Mikolaj Wieczorek ; Barbara Rychalska ; Jacek Dabrowski
CATEGORY: cs.CV [cs.CV, I.4.9; I.4.10]
HIGHLIGHT: In this paper we propose to use the mean centroid representation both during training and retrieval.

34, TITLE: End-to-End Approach for Recognition of Historical Digit Strings
AUTHORS: Mengqiao Zhao ; Andre G. Hochuli ; Abbas Cheddad
CATEGORY: cs.CV [cs.CV, cs.LG, eess.IV]
HIGHLIGHT: To this end, we propose an end-to-end segmentation-free deep learning approach to handle this challenging ancient handwriting style of dates present in the ARDIS dataset (4-digits long strings).

35, TITLE: Preserving Earlier Knowledge in Continual Learning with The Help of All Previous Feature Extractors
AUTHORS: Zhuoyun Li ; Changhong Zhong ; Sijia Liu ; Ruixuan Wang ; Wei-Shi Zheng
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: Recently, various approaches have been proposed to alleviate the catastrophic forgetting issue.

36, TITLE: FrameExit: Conditional Early Exiting for Efficient Video Recognition
AUTHORS: Amir Ghodrati ; Babak Ehteshami Bejnordi ; Amirhossein Habibian
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this paper, we propose a conditional early exiting framework for efficient video recognition.

37, TITLE: Robust Face-Swap Detection Based on 3D Facial Shape Information
AUTHORS: Weinan Guan ; Wei Wang ; Jing Dong ; Bo Peng ; Tieniu Tan
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a biometric information based method to fully exploit the appearance and shape feature for face-swap detection of key figures.

38, TITLE: Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
AUTHORS: Qin Wang ; Dengxin Dai ; Lukas Hoyer ; Olga Fink ; Luc Van Gool
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we leverage the guidance from self-supervised depth estimation, which is available on both domains, to bridge the domain gap.

39, TITLE: Two Stages for Visual Object Tracking
AUTHORS: Fei Chen ; Xiaodong Wang
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we propose a novel tracker with two-stages: detection and segmentation.

40, TITLE: KAMA: 3D Keypoint Aware Body Mesh Articulation
AUTHORS: Umar Iqbal ; Kevin Xie ; Yunrong Guo ; Jan Kautz ; Pavlo Molchanov
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows us to estimate a human body mesh from the positions of 3D body keypoints.

41, TITLE: Zero-Shot Detection Via Vision and Language Knowledge Distillation
AUTHORS: Xiuye Gu ; Tsung-Yi Lin ; Weicheng Kuo ; Yin Cui
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: We propose ViLD, a training method via Vision and Language knowledge Distillation.

42, TITLE: Image Inpainting By End-to-End Cascaded Refinement with Mask Awareness
AUTHORS: MANYU ZHU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To this end, we propose our novel mask-aware inpainting solution.

43, TITLE: Learning Synergistic Attention for Light Field Salient Object Detection
AUTHORS: YI ZHANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms.

44, TITLE: MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
AUTHORS: YAXING WANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs.

45, TITLE: Twins: Revisiting Spatial Attention Design in Vision Transformers
AUTHORS: XIANGXIANG CHU et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In this work, we revisit the design of the spatial attention and demonstrate that a carefully-devised yet simple spatial attention mechanism performs favourably against the state-of-the-art schemes.

46, TITLE: Deep Learning for Rheumatoid Arthritis: Joint Detection and Damage Scoring in X-rays
AUTHORS: Krzysztof Maziarz ; Anna Krason ; Zbigniew Wojna
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this work, we present a multi-task deep learning model that simultaneously learns to localize joints on X-ray images and diagnose two kinds of joint damage: narrowing and erosion.

47, TITLE: PDNet: Towards Better One-stage Object Detection with Prediction Decoupling
AUTHORS: LI YANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we analyze the suitable inference positions for object category and boundaries, and propose a prediction-target-decoupled detector named PDNet to establish a more flexible detection paradigm.

48, TITLE: High-Resolution Optical Flow from 1D Attention and Correlation
AUTHORS: Haofei Xu ; Jiaolong Yang ; Jianfei Cai ; Juyong Zhang ; Xin Tong
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a new method for high-resolution optical flow estimation with significantly less computation, which is achieved by factorizing 2D optical flow with 1D attention and correlation.

49, TITLE: A Deep Learning Object Detection Method for An Efficient Clusters Initializatio
AUTHORS: Hassan N. Noura ; Ola Salman ; Raphaël Couturier ; Abderrahmane Sider
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we apply the recent object detection Deep Learning (DL) model, named YOLO-v5, to detect the initial clustering parameters such as the number of clusters with their sizes and possible centroids.

50, TITLE: Medical Transformer: Universal Brain Encoder for 3D MRI Analysis
AUTHORS: Eunji Jun ; Seungwoo Jeong ; Da-Woon Heo ; Heung-Il Suk
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this work, we propose a novel transfer learning framework, called Medical Transformer, that effectively models 3D volumetric images in the form of a sequence of 2D image slices.

51, TITLE: DeRenderNet: Intrinsic Image Decomposition of Urban Scenes with Shape-(In)dependent Shading Rendering
AUTHORS: Yongjie Zhu ; Jiajun Tang ; Si Li ; Boxin Shi
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose DeRenderNet, a deep neural network to decompose the albedo and latent lighting, and render shape-(in)dependent shadings, given a single image of an outdoor urban scene, trained in a self-supervised manner.

52, TITLE: Exploring Relational Context for Multi-Task Dense Prediction
AUTHORS: David Bruggemann ; Menelaos Kanakis ; Anton Obukhov ; Stamatios Georgoulis ; Luc Van Gool
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks' relations.

53, TITLE: [Re] Don't Judge An Object By Its Context: Learning to Overcome Contextual Bias
AUTHORS: Sunnie S. Y. Kim ; Sharon Zhang ; Nicole Meister ; Olga Russakovsky
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To verify their performance, we attempted to reproduce all 12 tables in the original paper, including those in the appendix.

54, TITLE: Deep Domain Generalization with Feature-norm Network
AUTHORS: Mohammad Mahfujur Rahman ; Clinton Fookes ; Sridha Sridharan
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In this paper, we tackle the problem of training with multiple source domains with the aim to generalize to new domains at test time without an adaptation step.

55, TITLE: Revisiting Skeleton-based Action Recognition
AUTHORS: HAODONG DUAN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.

56, TITLE: Point Cloud Learning with Transformer
AUTHORS: Xian-Feng Han ; Yu-Jia Kuang ; Guo-Qiang Xiao
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we introduce a novel framework, called Multi-level Multi-scale Point Transformer (MLMSPT) that works directly on the irregular point clouds for representation learning.

57, TITLE: ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding
AUTHORS: Christos Sakaridis ; Dengxin Dai ; Luc Van Gool
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To address this, we introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions.

58, TITLE: DeepSatData: Building Large Scale Datasets of Satellite Images for Training Machine Learning Models
AUTHORS: Michail Tarasiou ; Stefanos Zafeiriou
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models with emphasis placed on dense classification tasks, e.g. semantic segmentation.

59, TITLE: Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification
AUTHORS: Amena Khatun ; Simon Denman ; Sridha Sridharan ; Clinton Fookes
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: To address this problem, we propose a semantic consistency and identity mapping multi-component generative adversarial network (SC-IMGAN) which provides style adaptation from one to many domains.

60, TITLE: Neural Ray-Tracing: Learning Surfaces and Reflectance for Relighting and View Synthesis
AUTHORS: Julian Knodt ; Seung-Hwan Baek ; Felix Heide
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we explicitly model the light transport between scene surfaces and we rely on traditional integration schemes and the rendering equation to reconstruct a scene.

61, TITLE: Interpretable Embedding Procedure Knowledge Transfer Via Stacked Principal Component Analysis and Graph Neural Network
AUTHORS: Seunghyun Lee ; Byung Cheol Song
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: This paper proposes a method of generating interpretable embedding procedure (IEP) knowledge based on principal component analysis, and distilling it based on a message passing neural network.

62, TITLE: ConTNet: Why Not Use Convolution and Transformer at The Same Time?
AUTHORS: HAOTIAN YAN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we innovatively propose ConTNet (ConvolutionTransformer Network), combining transformer with ConvNet architectures to provide large receptive fields.

63, TITLE: PAFNet: An Efficient Anchor-Free Object Detector Guidance
AUTHORS: YING XIN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we start from an anchor-free detector called TTFNet, modify the structure of TTFNet and introduce multiple existing tricks to realize effective server and mobile solutions respectively.

64, TITLE: Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies
AUTHORS: SORENA SARMADI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We describe an automated analysis method to quantify the detailed growth dynamics of a population of bacilliform bacteria.

65, TITLE: Efficient Pre-trained Features and Recurrent Pseudo-Labeling InUnsupervised Domain Adaptation
AUTHORS: Youshan Zhang ; Brian D. Davison
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we show how to efficiently opt for the best pre-trained features from seventeen well-known ImageNet models in unsupervised DA problems.

66, TITLE: Shot Contrastive Self-Supervised Learning for Scene Boundary Detection
AUTHORS: SHIXING CHEN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To address this challenge, we present a self-supervised shot contrastive learning approach (ShotCoL) to learn a shot representation that maximizes the similarity between nearby shots compared to randomly selected shots. To this end, we collected a new dataset called AdCuepoints with 3,975 movies and TV episodes, 2.2 million shots and 19,119 minimally disruptive ad cue-point labels.

67, TITLE: TRECVID 2020: A Comprehensive Campaign for Evaluating Video Retrieval Tasks Across Multiple Application Domains
AUTHORS: GEORGE AWAD et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: TRECVID 2020: A Comprehensive Campaign for Evaluating Video Retrieval Tasks Across Multiple Application Domains

68, TITLE: Pose-driven Attention-guided Image Generation for Person Re-Identification
AUTHORS: Amena Khatun ; Simon Denman ; Sridha Sridharan ; Clinton Fookes
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: To solve this issue, in this paper we propose an end-to-end pose-driven attention-guided generative adversarial network, to generate multiple poses of a person.

69, TITLE: Extreme Rotation Estimation Using Dense Correlation Volumes
AUTHORS: Ruojin Cai ; Bharath Hariharan ; Noah Snavely ; Hadar Averbuch-Elor
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap.

70, TITLE: Boosting Co-teaching with Compression Regularization for Label Noise
AUTHORS: Yingyi Chen ; Xi Shen ; Shell Xu Hu ; Johan A. K. Suykens
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we study the problem of learning image classification models in the presence of label noise.

71, TITLE: Segmentation-Based Bounding Box Generation for Omnidirectional Pedestrian Detection
AUTHORS: Masato Tamura ; Tomoaki Yoshinaga
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose a segmentation-based bounding box generation method for omnidirectional pedestrian detection, which enables detectors to tightly fit bounding boxes to pedestrians without omnidirectional images for training.

72, TITLE: MOD: Benchmark for Military Object Detection
AUTHORS: Xin Yi ; Jiahao Wu ; Bo Ma ; Yangtong Ou ; Longyao Liu
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To facilitate future military object detection research, we propose a novel, publicly available object detection benchmark in military filed called MOD, which contains 6,000 images and 17,465 labeled instances.

73, TITLE: Sign Segmentation with Changepoint-Modulated Pseudo-Labelling
AUTHORS: Katrin Renz ; Nicolaj C. Stache ; Neil Fox ; Gül Varol ; Samuel Albanie
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: The objective of this work is to find temporal boundaries between signs in continuous sign language.

74, TITLE: Inpainting Transformer for Anomaly Detection
AUTHORS: Jonathan Pirnay ; Keng Chai
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In particular we pose anomaly detection as a patch-inpainting problem and propose to solve it with a purely self-attention based approach discarding convolutions.

75, TITLE: ZePHyR: Zero-shot Pose Hypothesis Rating
AUTHORS: Brian Okorn ; Qiao Gu ; Martial Hebert ; David Held
CATEGORY: cs.CV [cs.CV, cs.RO]
HIGHLIGHT: To address these issues, we introduce a novel method for zero-shot object pose estimation in clutter.

76, TITLE: Does Face Recognition Error Echo Gender Classification Error?
AUTHORS: Ying Qiu ; Vítor Albiero ; Michael C. King ; Kevin W. Bowyer
CATEGORY: cs.CV [cs.CV, cs.AI, cs.CY]
HIGHLIGHT: We analyze results from three different gender classification algorithms (one open-source and two commercial), and two face recognition algorithms (one open-source and one commercial), on image sets representing four demographic groups (African-American female and male, Caucasian female and male).

77, TITLE: The Evolution of Rumors on A Closed Platform During COVID-19
AUTHORS: Andrea W Wang ; Jo-Yu Lan ; Chihhao Yu ; Ming-Hung Wang
CATEGORY: cs.CY [cs.CY, cs.CL, cs.LG, cs.SI]
HIGHLIGHT: In this work we looked into a dataset of 114 thousands of suspicious messages collected from the most popular closed messaging platform in Taiwan between January and July, 2020.

78, TITLE: Meta-evaluation of Conversational Search Evaluation Metrics
AUTHORS: Zeyang Liu ; Ke Zhou ; Max L. Wilson
CATEGORY: cs.IR [cs.IR, cs.AI, cs.LG]
HIGHLIGHT: In this paper, we systematically meta-evaluate a variety of conversational search metrics.

79, TITLE: Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers
AUTHORS: Navid Rekabsaz ; Simone Kopeinik ; Markus Schedl
CATEGORY: cs.IR [cs.IR, cs.AI, cs.LG]
HIGHLIGHT: We conduct experiments on two passage retrieval collections (MS MARCO Passage Re-ranking and TREC Deep Learning 2019 Passage Re-ranking), which we extend by fairness annotations of a selected subset of queries regarding gender attributes.

80, TITLE: Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification
AUTHORS: JIE CHEN et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we consider the label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention.

81, TITLE: Policy Manifold Search: Exploring The Manifold Hypothesis for Diversity-based Neuroevolution
AUTHORS: Nemanja Rakicevic ; Antoine Cully ; Petar Kormushev
CATEGORY: cs.LG [cs.LG, cs.NE]
HIGHLIGHT: This paper proposes a novel method for diversity-based policy search via Neuroevolution, that leverages learned representations of the policy network parameters, by performing policy search in this learned representation space.

82, TITLE: A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
AUTHORS: Andrew Patterson ; Adam White ; Sina Ghiassian ; Martha White
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this work, we build on these insights and introduce a new generalized PBE, that extends the linear PBE to the nonlinear setting.

83, TITLE: Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
AUTHORS: MICHAEL R. ZHANG et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, stat.ML]
HIGHLIGHT: In this paper, we challenge this conditional independence assumption and propose a family of expressive autoregressive dynamics models that generate different dimensions of the next state and reward sequentially conditioned on previous dimensions.

84, TITLE: Text Generation with Deep Variational GAN
AUTHORS: Mahmoud Hossam ; Trung Le ; Michael Papasimeon ; Viet Huynh ; Dinh Phung
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CL, I.2.0; I.2.7; I.5.0]
HIGHLIGHT: In this paper we propose a GAN-based generic framework to address the problem of mode-collapse in a principled approach.

85, TITLE: Improved and Efficient Text Adversarial Attacks Using Target Information
AUTHORS: Mahmoud Hossam ; Trung Le ; He Zhao ; Viet Huynh ; Dinh Phung
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CL, cs.CR, I.5.0, I.2.0]
HIGHLIGHT: In this paper we study the effect of leveraging the target model outputs and data on both attack rates and average number of queries, and we show that both can be improved, with a limited overhead of additional queries.

86, TITLE: Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
AUTHORS: Michael M. Bronstein ; Joan Bruna ; Taco Cohen ; Petar Veli?kovi?
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CG, cs.CV, stat.ML]
HIGHLIGHT: Such a 'geometric unification' endeavour, in the spirit of Felix Klein's Erlangen Program, serves a dual purpose: on one hand, it provides a common mathematical framework to study the most successful neural network architectures, such as CNNs, RNNs, GNNs, and Transformers.

87, TITLE: SpikE: Spike-based Embeddings for Multi-relational Graph Data
AUTHORS: Dominik Dold ; Josep Soler Garrido
CATEGORY: cs.NE [cs.NE, cs.LG, q-bio.NC]
HIGHLIGHT: We propose a spike-based algorithm where nodes in a graph are represented by single spike times of neuron populations and relations as spike time differences between populations.

88, TITLE: Entropy-Based Evolutionary Diversity Optimisation for The Traveling Salesperson Problem
AUTHORS: Adel Nikfarjam ; Jakob Bossek ; Aneta Neumann ; Frank Neumann
CATEGORY: cs.NE [cs.NE]
HIGHLIGHT: In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem.

89, TITLE: End-to-End Intersection Handling Using Multi-Agent Deep Reinforcement Learning
AUTHORS: Alessandro Paolo Capasso ; Paolo Maramotti ; Anthony Dell'Eva ; Alberto Broggi
CATEGORY: cs.RO [cs.RO, cs.AI, cs.LG]
HIGHLIGHT: In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided.

90, TITLE: Interactive Visualization for Exploring Information Fragments in Software Repositories
AUTHORS: YOUNGTAEK KIM et. al.
CATEGORY: cs.SE [cs.SE, cs.CV]
HIGHLIGHT: We introduce ExIF, an interactive visualization for exploring information fragments in software repositories.

91, TITLE: DASEE A Synthetic Database of Domestic Acoustic Scenes and Events in Dementia Patients Environment
AUTHORS: Abigail Copiaco ; Christian Ritz ; Stefano Fasciani ; Nidhal Abdulaziz
CATEGORY: eess.AS [eess.AS, cs.AI, cs.SD, eess.SP]
HIGHLIGHT: In this work, we detail our approach on generating an unbiased synthetic domestic audio database, consisting of sound scenes and events, emulated in both quiet and noisy environments. As a result, we present an 11-class database containing excerpts of clean and noisy signals at 5-seconds duration each, uniformly sampled at 16 kHz.

92, TITLE: IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research
AUTHORS: JAKOB ABESSER et. al.
CATEGORY: eess.AS [eess.AS, cs.AI, cs.SD]
HIGHLIGHT: In this paper, we present a novel open benchmark dataset, containing 2.5 hours of stereo audio recordings of 4718 vehicle passing events captured with both high-quality sE8 and medium-quality MEMS microphones.

93, TITLE: LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images
AUTHORS: YANGLAN OU et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: To tackle this problem, we propose a new neural network architecture tailored for segmenting highly-discontinuous 2.5D data such as DWIs.

94, TITLE: Deep Learning Body Region Classification of MRI and CT Examinations
AUTHORS: PHILIPPE RAFFY et. al.
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: We show that deep learning models can classify CT and MRI images by body region including lower and upper extremities with high accuracy.

95, TITLE: Multi-scale Deep Learning Architecture for Nucleus Detection in Renal Cell Carcinoma Microscopy Image
AUTHORS: Shiba Kuanar ; Vassilis Athitsos ; Dwarikanath Mahapatra ; Anand Rajan
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: In this paper, we introduce a deep learning-based detection model for cell classification on IHC stained histology images.

96, TITLE: Image Synthesis As A Pretext for Unsupervised Histopathological Diagnosis
AUTHORS: Dejan Stepec ; Danijel Skocaj
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this work we evaluate a crucial part of the unsupervised visual anomaly detection pipeline, that is needed for normal appearance modeling, as well as the ability to reconstruct closest looking normal and tumor samples.

97, TITLE: Deep Two-Stage High-Resolution Image Inpainting
AUTHORS: Andrey Moskalenko ; Mikhail Erofeev ; Dmitriy Vatolin
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this article, we propose a method that solves the problem of inpainting arbitrary-size images.

98, TITLE: Unsupervised Detection of Cancerous Regions in Histology Imagery Using Image-to-Image Translation
AUTHORS: Dejan Stepec ; Danijel Skocaj
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.

99, TITLE: Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes
AUTHORS: Ruixu Geng ; Yang Hu ; Yan Chen
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: This paper presents a comprehensive overview of both conventional and deep learning-based NLOS imaging techniques.