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Computer Vision ACCV 2018-Part I.pdf
Preface
Organization
Contents – Part I
Oral Session O1: Learning
Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation
1 Introduction
2 Related Work
3 G2GAN: Dual Generator Generative Adversary Networks
3.1 Model Formulation
3.2 Model Optimization
3.3 Implementation Details
4 Experiments
4.1 Experimental Setup
4.2 Comparison with the State-of-the-Art on Different Tasks
4.3 Model Analysis
5 Conclusion
References
Pioneer Networks: Progressively Growing Generative Autoencoder
1 Introduction
2 Related Work
3 Pioneer Networks
3.1 Intuition
3.2 Encoder–Decoder Losses
3.3 Model and Training
4 Experiments
4.1 CelebA and CelebA-HQ
4.2 LSUN Bedrooms
4.3 Cifar-10
5 Discussion and Conclusion
References
Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously
1 Introduction
2 Related Work
2.1 Conditional GANs
2.2 Facial Attribute and Generation
3 Background
3.1 Connection Network
3.2 Selective Learning for Classification
4 Editable Generative Adversarial Networks
4.1 Formulation
5 Experiments
5.1 Qualitative Evaluation
5.2 Quantitative Evaluation
6 Conclusion
References
Cross Connected Network for Efficient Image Recognition
1 Introduction
2 Related Work
3 Cross Connected Network
3.1 Cross Connection
3.2 Pod Structure
3.3 Receptive Fields of Depthwise Convolutions
3.4 CrossNet Architecture
4 Implementation
5 Experiments
5.1 ImageNet-1k Dataset
5.2 MSCOCO Dataset
6 Conclusions
References
Answer Distillation for Visual Question Answering
1 Introduction
2 Related Work
3 Answer Distillation for Visual Question Answering
3.1 Overview
3.2 Answer Distillation
3.3 Answer Prediction
4 Experiments
4.1 Basic Configuration
4.2 Results
5 Conclusion
References
Spiral-Net with F1-Based Optimization for Image-Based Crack Detection
1 Introduction
1.1 Related Works
2 Spiral-Net
3 F1-Based Optimization
3.1 F1-Based Loss
3.2 F1-Guided Gradient Weighting
4 Experimental Results
4.1 Crack Dataset
4.2 Implementation Details
4.3 Performance Analysis on Spiral-Net
4.4 Comparison to Other Methods
5 Conclusion
References
Flex-Convolution
1 Introduction
2 Related Work
3 Method
3.1 Convolution Layer
3.2 Extending Sub-sampling to Irregular Data
4 Implementation
4.1 Neighborhood Processing
4.2 Efficient Implementation of Layer Primitives
4.3 Network Architecture for Large-Scale Semantic Segmentation
5 Experiments
5.1 Synthetic Data
5.2 Real-World Semantic Point Cloud Segmentation
6 Conclusion
References
Poster Session P1
Extreme Reverse Projection Learning for Zero-Shot Recognition
1 Introduction
2 Related Work
3 The Proposed Model
3.1 Problem Definition
3.2 Projection Learning
3.3 Model Formulation
3.4 Optimization
3.5 Discussion
4 Experiments
4.1 Datasets and Settings
4.2 Results Under Standard ZSL Setting
4.3 Results Under Pure ZSL Setting
4.4 Results Under Generalized ZSL Setting
4.5 Further Evaluations
5 Conclusion
References
A Defect Inspection Method for Machine Vision Using Defect Probability Image with Deep Convolutional Neural Network
1 Introduction
2 Machine Vision System
3 Related Work
4 Method
4.1 Defect Probability Image
4.2 Machine Vision and Deep Convolutional Neural Network Integration
5 Experiments
5.1 Database and Experimental Setup
5.2 Results and Discussion
6 Conclusion
References
3D Pick & Mix: Object Part Blending in Joint Shape and Image Manifolds
1 Introduction
2 Related Work
3 Overview
4 Methodology
4.1 Building Shape Manifolds for Object Parts
4.2 Building Shape Manifolds of Parts
4.3 Learning to Embed Images into the Shape Manifolds
4.4 Shape Blending Through Cross-Manifold Optimization
5 Results
5.1 Quantitative Results on Image-Based Shape Retrieval
5.2 Qualitative Results on Image-Based Shape Retrieval
6 Conclusions
References
Minutiae-Based Gender Estimation for Full and Partial Fingerprints of Arbitrary Size and Shape
1 Introduction
2 Related Work
3 Methodology
3.1 Proposed Concept
3.2 Feature Extraction and Model Building
3.3 Minutiae Ensemble Fusion
4 Experimental Setup
5 Results
6 Conclusion
References
Simultaneous Face Detection and Head Pose Estimation: A Fast and Unified Framework
1 Introduction
2 Related Work
2.1 Face Detection
2.2 Head Pose Estimation
3 Proposed Framework
3.1 Network Architecture
3.2 Pose Class
3.3 Training
3.4 Testing
4 Experimental Validations
4.1 Datasets and Evaluation Metric
4.2 Setting
4.3 Face Detection
4.4 Head Pose Estimation
4.5 Does Pose Class Help?
5 Conclusions
References
Progressive Feature Fusion Network for Realistic Image Dehazing
1 Introduction
2 Related Work
3 Progressive Feature Fusion Network (PFFNet) for Image Dehazing
4 Experiments
4.1 Dataset
4.2 Comparisons and Analysis
5 Conclusion
References
Semi-supervised Learning for Face Sketch Synthesis in the Wild
1 Introduction
2 Related Works
2.1 Exemplar Based Methods
2.2 Learning Based Methods
3 Semi-supervised Face Sketch Synthesis
3.1 Overview
3.2 Pseudo Sketch Feature Generator
3.3 Loss Functions
4 Implementation Details
4.1 Datasets
4.2 Patch Matching
4.3 Training Details
5 Evaluation on Public Benchmarks
5.1 Qualitative Comparison
5.2 Quantitative Comparison
6 Sketch Synthesis in the Wild
6.1 Qualitative Comparison
6.2 Effectiveness of Additional Training Photos
6.3 Mean Opinion Score Test
7 Conclusion
References
Evolvement Constrained Adversarial Learning for Video Style Transfer
1 Introduction
2 Related Works
3 Overview
4 The Evolve-Sync Loss
5 Video Style Transfer GAN (VST-GAN)
5.1 Architecture
5.2 Training
6 Experiments
6.1 Qualitative Comparison
6.2 Quantitative Comparison
7 Conclusion
References
An Unsupervised Deep Learning Framework via Integrated Optimization of Representation Learning and GMM-Based Modeling
1 Introduction
2 Related Work
3 The Proposed Approach
3.1 GMM
3.2 Representation Learning and GMM-Based Modeling
3.3 Network Structure
4 Experiments
4.1 Dataset
4.2 Implementation Details
4.3 Benchmarks
4.4 Performance
4.5 Discussion
5 Conclusion
References
Recovering Affine Features from Orientation- and Scale-Invariant Ones
1 Introduction
2 Theoretical Background
3 Recovering Affine Correspondences
3.1 Affine Transformation Model
3.2 Affine Correspondence from Epipolar Geometry
4 Experimental Results
4.1 Comparing Techniques to Estimate Affine Correspondences
4.2 Application: Homography Estimation
5 Conclusion
References
Totally Looks Like - How Humans Compare, Compared to Machines
1 Introduction
2 Related Work
3 Method
3.1 Dataset
3.2 Image Retrieval
4 Experiments
4.1 Feature Extraction
4.2 Matching Images
4.3 Human Experiments
5 Discussion
References
Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs Using Multi-scale and Conditional Adversarial Network
1 Introduction
1.1 Related Work
1.2 Contributions
2 Methods
2.1 Multi-scale and Conditional Adversarial Network (MCA-Net)
2.2 Bone suppression with Cross Projection Tensor
3 Data and Training Details
4 Experiments and Results
4.1 Algorithm Evaluation
4.2 Clinical Evaluation
5 Discussion
6 Conclusion
References
GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds
1 Introduction
2 Related Work
2.1 Human Behaviour Prediction
2.2 Group Detection
3 Architecture
3.1 Neighbourhood Modelling
3.2 Trajectory Prediction
3.3 Group Detection
4 Evaluation and Discussion
4.1 Implementation Details
4.2 Evaluation of the Trajectory Prediction
4.3 Evaluation of the Group Detection
4.4 Ablation Experiment
4.5 Time Efficiency
5 Conclusions
References
Multi-level Sequence GAN for Group Activity Recognition
1 Introduction
2 Related Work
3 Methodology
3.1 Action Codes
3.2 Semi-supervised GAN Architecture
3.3 GAN Objectives
4 Experiments
4.1 Datasets
4.2 Metrics
4.3 Network Architecture and Training
4.4 Results
4.5 Ablation Experiments
4.6 Time Efficiency
5 Conclusions
References
Spatio-Temporal Fusion Networks for Action Recognition
1 Introduction
2 Related Work
3 Approach
3.1 Spatio-Temporal Fusion Networks
3.2 STFN Components
4 Experiments
4.1 Datasets
4.2 Implementation Details
4.3 Evaluation of Different Designs
4.4 Evaluation of Fusion Operations
4.5 Evaluation of Fusion Directions
4.6 Evaluation of a Number of Segments
4.7 Base Performance of Two-Stream Network
4.8 Comparison with the State-of-the-art
5 Conclusion
References
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
1 Introduction
2 Related Work
3 Proposed Learning Framework
3.1 3D Mesh Embedding
3.2 Collecting Synthetic Data
3.3 Learning the Image Latent Space
3.4 Learning the Index to Select a Model
3.5 Learning the Shape Parameters
4 Experiments
4.1 Estimating the Image Latent Space
4.2 Selecting a 3D Mesh
4.3 Estimating the Shape Parameters
4.4 3D Reconstruction from a Single Image
4.5 Implementation Details and Limitations
5 Discussion
6 Conclusion
References
Face Completion with Semantic Knowledge and Collaborative Adversarial Learning
1 Introduction
2 Related Work
3 Collaborative Face Completion
3.1 Collaborative GAN
3.2 Reconstruction Loss
3.3 Inpainting Concentrated Generation
3.4 Network Architecture
4 Experimental Results
4.1 Datasets
4.2 Models
4.3 Face Completion
5 Conclusion
References
Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal
1 Introduction
2 Related Work
2.1 Classical Image Binarization Based Approaches
2.2 Parametric Reconstruction Based Approaches
2.3 Background Shading Estimation Based Approaches
3 Water-Filling Algorithm
3.1 Modeling by Diffusion Equation
4 Experiments and Performance Evaluation
4.1 Datasets
4.2 Methods for Comparison
4.3 Parameters Tuning
4.4 Results Analysis
5 Conclusions
References
Matchable Image Retrieval by Learning from Surface Reconstruction
1 Introduction
2 Related Works
3 The GL3D Benchmark Dataset
4 Method
4.1 Problem Formulation
4.2 Network Architecture
4.3 Fine-Grained Training Data Generation
4.4 Learning with Batched Hard Mining
4.5 Pre-matching Regional Code (PRC)
5 Experiments
5.1 Distinctiveness of Matchable Image Retrieval
5.2 Experiments for Matchable Image Retrieval
5.3 Integration of Matchable Image Retrieval with SfM
6 Conclusions
References
Thinking Outside the Box: Generation of Unconstrained 3D Room Layouts
1 Introduction
2 Related Work
3 Planar R-CNN
3.1 Network Architecture
3.2 Implementation
4 3D Room Layout Generation
4.1 Spatial Voting
5 Experimental Evaluation
5.1 Training Using SUN RGB-D
5.2 2D Room Layout Estimation with NYUDv2 303
5.3 Whole Room Layout Estimation Using ScanNet
5.4 Results
6 Conclusions
References
VIENA2: A Driving Anticipation Dataset
1 Introduction
2 VIENA2
2.1 Scenarios and Data Collection
2.2 Comparison to Other Datasets
3 Benchmark Algorithms
3.1 Baseline Methods
3.2 A New Multi-modal LSTM
3.3 Action Modeling
3.4 Implementation Details
4 Benchmark Evaluation and Analysis
4.1 Action Anticipation on VIENA2
4.2 Challenges of VIENA2
4.3 Benefits of VIENA2 for Anticipation from Real Images
4.4 Bias Analysis
5 Conclusion
References
Multilevel Collaborative Attention Network for Person Search
1 Introduction
2 Related Work
3 Multilevel Collaborative Attention Network
3.1 Multilevel Selective Learning
3.2 Collaborative Attention Learning
3.3 Online Hard Mined Random Sampling Softmax
4 Experiments
4.1 Datasets and Evaluation Metrics
4.2 Implementation Details
4.3 Comparison with State-of-the-art Methods
4.4 Ablation Study
5 Conclusions
References
Enhancing Perceptual Attributes with Bayesian Style Generation
1 Introduction
2 Related Works
3 Enhancing Perceptual Attributes with BAE
3.1 Arbitrary Style Transfer
3.2 Bayesian Attribute Enhancement
3.3 Implementation
4 Experimental Validation
4.1 Experimental Setup and Datasets
4.2 Results
5 Conclusions
References
Deep Convolutional Compressed Sensing for LiDAR Depth Completion
1 Introduction
2 Related Work
2.1 Compressed Sensing
2.2 Deep Learning
3 Preliminary
3.1 Compressed Sensing
3.2 Deep Component Analysis
4 Deep Convolutional Compressed Sensing
4.1 Inference
4.2 Learning
5 Experiments
5.1 Implementation Details
5.2 KITTI Depth Completion Benchmark
5.3 Effect of Amount of Training Data
5.4 Effect of Iterative Optimization
6 Conclusion
References
Learning for Video Super-Resolution Through HR Optical Flow Estimation
1 Introduction
2 Related Work
2.1 Single Image SR
2.2 Video SR
3 Network Architecture
3.1 Optical Flow Reconstruction Net (OFRnet)
3.2 Motion Compensation
3.3 Super-Resolution Net (SRnet)
3.4 Loss Function
4 Experiments
4.1 Datasets
4.2 Implementation Details
4.3 Ablation Study
4.4 Comparisons to the State-of-the-Art
5 Conclusions and Future Work
References
Deep Multiple Instance Learning for Zero-Shot Image Tagging
1 Introduction
2 Related Work
3 Our Method
3.1 Problem Formulation
3.2 Network Architecture
4 Experiment
4.1 Setup
4.2 Tagging Performance
4.3 Zero Shot Recognition (ZSR)
4.4 Discussion
5 Conclusion
References
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
1 Introduction
2 Problem Description
3 Zero-Shot Detection
3.1 Model Architecture
3.2 Training and Inference
3.3 ZSD Without Pre-defined Unseen
4 Experiments
4.1 Dataset and Experiment Protocol
4.2 ZSD Performance
4.3 Zero Shot Recognition (ZSR)
4.4 Challenges and New Directions
5 Conclusion
References
Vision-Based Freezing of Gait Detection with Anatomic Patch Based Representation
1 Introduction
2 Related Works
2.1 Vision Based Parkinsonian Gait Analysis
2.2 Deep Learning Based Human Action Recognition
3 Proposed Method
3.1 Anatomic Joint Patches
3.2 Weakly-Supervised Learning for Patch Proposals
3.3 Patch and Global Feature Fusion
4 Experimental Results and Discussions
4.1 Dataset
4.2 Implementation Details
4.3 FoG Detection Results
4.4 Key Patch Localization
4.5 Comparison
5 Conclusion
References
Fast Video Shot Transition Localization with Deep Structured Models
1 Introduction
2 Related Work
3 Our Approach
3.1 An Overview
3.2 Initial Filtering
3.3 Cut Model
3.4 Gradual Model
4 ClipShots
5 Experiments
5.1 Databases and Evaluation Metrics
5.2 Experiments Configuration
5.3 Experiments on ClipShots
5.4 Experiments on TRECVID07
5.5 Experiments on RAI
6 Conclusion
References
Traversing Latent Space Using Decision Ferns
1 Introduction
2 Related Work
2.1 Learning Representations for Complex Inference Tasks
2.2 Decision Forests and Ferns
3 Background
3.1 Variational Autoencoders
3.2 Decision Forests and Ferns
4 Operating in Latent Space
4.1 Constructing a Latent Space
4.2 Traversing the Latent Space
4.3 Latent Space Traversal Network
5 Experiments
5.1 Imposing Spatial Transformation
5.2 Imposing Kinematics
5.3 Latent Space for Prediction
6 Conclusion
References
Parallel Convolutional Networks for Image Recognition via a Discriminator
1 Introduction
2 Related Work
3 D-PCN
3.1 Motivation
3.2 Architecture
3.3 Training Method
4 Experiments
4.1 Classification Results on CIFAR-100
4.2 Classification Results on ImageNet
4.3 Visualization
4.4 Segmentation on PASCAL VOC 2012
5 Conclusion
References
ENG: End-to-End Neural Geometry for Robust Depth and Pose Estimation Using CNNs
1 Introduction
2 Related Work
2.1 Single Image Depth Prediction
2.2 Optical Flow Prediction
2.3 Pose Estimation
3 Method
3.1 Network Architecture
3.2 Depth Prediction
3.3 Flow Prediction
3.4 Pose Estimation
3.5 Loss Functions
3.6 Training Regime
4 Results
4.1 Depth Estimation
4.2 Pose Estimation
4.3 Ablation Experiments
5 Conclusion and Further Work
References
Multi-scale Adaptive Structure Network for Human Pose Estimation from Color Images
1 Introduction
2 Related Works
3 Method
3.1 Adaptive Heatmaps
3.2 Limb Region
3.3 Multi-scale Adaptive Structure Supervision
4 Experiments
4.1 Implementation Details
4.2 Results
4.3 Ablation Study
4.4 Discussion
5 Conclusions
References
Full Explicit Consistency Constraints in Uncalibrated Multiple Homography Estimation
1 Introduction
2 Path to Constraints
3 Problem
4 Algebraic Prerequisites
4.1 The Characteristic Polynomial
4.2 A Double Root of the Characteristic Polynomial of a Cubic Polynomial
5 Full Constraints
6 Maximum Likelihood Estimation
7 Experiments
8 Conclusion
References
A New Method for Computing the Principal Point of an Optical Sensor by Means of Sphere Images
1 Introduction
2 How the Perspective Image of a Sphere Is Related to the Camera Intrinsics
3 The Locus of the Principal Point Determined by Two Sphere Images
4 Determination of the Principal Point by Three Sphere Images
5 Synthetic Experiments
6 Real Experiments
7 Conclusions
References
NightOwls: A Pedestrians at Night Dataset
1 Introduction
2 Related Work
3 Dataset
4 Experiments
5 Conclusion
References
Multi-view Consensus CNN for 3D Facial Landmark Placement
1 Introduction
2 Methods
2.1 Multi-view Rendering
2.2 Geometric Derivatives
2.3 Network Architecture and Loss Function
2.4 Landmark Detection and Consensus Estimation
3 Data
4 Results and Discussion
5 Conclusion
References
Author Index
C. V. Jawahar
Hongdong Li
Greg Mori
Konrad Schindler (Eds.)
1
6
3
1
1
S
C
N
L
Computer Vision –
ACCV 2018
14th Asian Conference on Computer Vision
Perth, Australia, December 2–6, 2018
Revised Selected Papers, Part I
Lecture Notes in Computer Science
11361
Commenced Publication in 1973
Founding and Former Series Editors:
Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen
Editorial Board Members
David Hutchison
Lancaster University, Lancaster, UK
Takeo Kanade
Carnegie Mellon University, Pittsburgh, PA, USA
Josef Kittler
University of Surrey, Guildford, UK
Jon M. Kleinberg
Cornell University, Ithaca, NY, USA
Friedemann Mattern
ETH Zurich, Zurich, Switzerland
John C. Mitchell
Stanford University, Stanford, CA, USA
Moni Naor
Weizmann Institute of Science, Rehovot, Israel
C. Pandu Rangan
Indian Institute of Technology Madras, Chennai, India
Bernhard Steffen
TU Dortmund University, Dortmund, Germany
Demetri Terzopoulos
University of California, Los Angeles, CA, USA
Doug Tygar
University of California, Berkeley, CA, USA
More information about this series at http://www.springer.com/series/7412
C. V. Jawahar Hongdong Li
Greg Mori Konrad Schindler (Eds.)
Computer Vision –
ACCV 2018
14th Asian Conference on Computer Vision
Perth, Australia, December 2–6, 2018
Revised Selected Papers, Part I
123
Editors
C. V. Jawahar
IIIT Hyderabad
Hyderabad, India
Greg Mori
Simon Fraser University
Burnaby, BC, Canada
Hongdong Li
ANU
Canberra, ACT, Australia
Konrad Schindler
ETH Zurich
Zurich, Zürich, Switzerland
ISSN 0302-9743
Lecture Notes in Computer Science
ISBN 978-3-030-20886-8
https://doi.org/10.1007/978-3-030-20887-5
ISBN 978-3-030-20887-5
(eBook)
ISSN 1611-3349
(electronic)
LNCS Sublibrary: SL6 – Image Processing, Computer Vision, Pattern Recognition, and Graphics
© Springer Nature Switzerland AG 2019
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Preface
The Asian Conference on Computer Vision (ACCV) 2018 took place in Perth,
Australia, during December 2–6, 2018. The conference featured novel research con-
tributions from almost all sub-areas of computer vision.
This year we received a record number of conference submissions. After removing
the desk rejects, 979 valid, complete manuscripts were submitted for review. A pool of
34 area chairs and 1,063 reviewers was recruited to conduct paper reviews. Like
previous editions of ACCV, we adopted a double-blind review process to determine
which of these papers to accept. Identities of authors were not visible to reviewers and
area chairs; nor were the identities of the assigned reviewers and area chairs visible to
authors. The program chairs did not submit papers to the conference.
Each paper was reviewed by at least three reviewers. Authors were permitted to
respond to the initial reviews during a rebuttal period. After this, the area chairs led
discussions among reviewers. Finally, a physical area chairs was held in Singapore,
during which panels of three area chairs deliberated to decide on acceptance decisions
for each paper. At the end of this process, 274 papers were accepted for publication in
the ACCV 2018 conference proceedings, of which five were later withdrawn by their
authors.
In addition to the main conference, ACCV 2018 featured 11 workshops and six
tutorials.
We would like to thank all the organizers, sponsors, area chairs, reviewers, and
authors. Special
thanks go to Prof. Guosheng Lin from Nanyang Technological
University, Singapore, for hosting the area chair meeting. We acknowledge the support
of Microsoft’s Conference Management Toolkit (CMT) team for providing the
software used to manage the review process.
We greatly appreciate the efforts of all
those who contributed to making the
conference a success.
December 2018
C. V. Jawahar
Hongdong Li
Greg Mori
Konrad Schindler
Organization
Seoul National University, South Korea
University of Western Australia, Australia
University of Adelaide, Australia
University of Tokyo, Japan
IIIT Hyderabad, India
Australian National University, Australia
Simon Fraser University and Borealis AI, Canada
ETH Zurich, Switzerland
General Chairs
Kyoung-mu Lee
Ajmal Mian
Ian Reid
Yoichi Sato
Program Chairs
C. V. Jawahar
Hongdong Li
Greg Mori
Konrad Schindler
Advisor
Richard Hartley
Australian National University, Australia
Publication Chair
Hamid Rezatofighi
University of Adelaide, Australia
Local Arrangements Chairs
Guosheng Lin
Ajmal Mian
Area Chairs
Lourdes Agapito
Xiang Bai
Vineeth N.
Balasubramanian
Gustavo Carneiro
Tat-Jun Chin
Minsu Cho
Bohyung Han
Junwei Han
Mehrtash Harandi
Gang Hua
Nanyang Technological University, Singapore
University of Western Australia, Australia
University College London, UK
Huazhong University of Science and Technology,
China
IIT Hyderabad, India
University of Adelaide, Australia
University of Adelaide, Australia
POSTECH, South Korea
Seoul National University, South Korea
Northwestern Polytechnical University, China
Monash University, Australia
Microsoft Research, Asia
viii
Organization
Rei Kawakami
Tae-Kyun Kim
Junseok Kwon
Florent Lafarge
Laura Leal-Taixé
Zhouchen Lin
Yanxi Liu
Oisin Mac Aodha
Anurag Mittal
Vinay Namboodiri
P. J. Narayanan
Carl Olsson
Imari Sato
Shiguang Shan
Chunhua Shen
Boxin Shi
Terence Sim
Yusuke Sugano
Min Sun
Robby Tan
Siyu Tang
Radu Timofte
Jingyi Yu
Junsong Yuan
University of Tokyo, Japan
Imperial College London, UK
Chung-Ang University, South Korea
Inria, France
TU Munich, Germany
Peking University, China
Penn State University, USA
Caltech, USA
IIT Madras, India
IIT Kanpur, India
IIIT Hyderabad, India
Lund University, Sweden
National Institute of Informatics
Chinese Academy of Sciences, China
University of Adelaide, Australia
Peking University, China
National University of Singapore, Singapore
Osaka University, Japan
National Tsing Hua University, Taiwan
Yale-NUS College, USA
MPI for Intelligent Systems
ETH Zurich, Switzerland
University of Delaware, USA
State University of New York at Buffalo, USA
Additional Reviewers
Ehsan Abbasnejad
Akash Abdu Jyothi
Abrar Abdulnabi
Nagesh Adluru
Antonio Agudo
Unaiza Ahsan
Hai-zhou Ai
Alexandre Alahi
Xavier Alameda-Pineda
Andrea Albarelli
Mohsen Ali
Saad Ali
Mitsuru Ambai
Cosmin Ancuti
Vijay Rengarajan Angarai
Pichaikuppan
Michel Antunes
Djamila Aouada
Ognjen Arandjelovic
Anil Armagan
Chetan Arora
Mathieu Aubry
Hossein Azizpour
Seung-Hwan Baek
Aijun Bai
Peter Bajcsy
Amr Bakry
Vassileios Balntas
Yutong Ban
Arunava Banerjee
Monami Banerjee
Atsuhiko Banno
Aayush Bansal
Dániel Baráth
Lorenzo Baraldi
Adrian Barbu
Nick Barnes
Peter Barnum
Joe Bartels
Paul Beardsley
Sima Behpour
Vasileios Belagiannis
Boulbaba Ben Amor
Archith Bency
Ryad Benosman
Gedas Bertasius
Ross Beveridge
Binod Bhattarai
Arnav Bhavsar
Simone Bianco
Oliver Bimber
Tolga Birdal
Horst Bischof
Arijit Biswas
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