Computer Vision - ECCV 2020

Name: Computer Vision - ECCV 2020 | 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XIII
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16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XIII

Andrea Vedaldi Horst Bischof Thomas Brox Jan-Michael Frahm(Editor)

Springer (Publisher)

Published on 27. November 2020

XLII, 799 pages

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Content

Intro
Foreword
Preface
Organization
Contents - Part XIII
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards
1 Introduction
2 Related Work
3 The FAshion CAptioning Dataset
3.1 Data Collection, Labeling and Pre-Processing
3.2 Comparison with Other Datasets
4 Respecting Semantics for Fashion Captioning
4.1 Basic Problem Formulation
4.2 Attribute Embedding
4.3 Increasing the Accuracy of Captioning with Semantic Rewards
4.4 Joint Training of MLE and RL.
5 Experiments
5.1 Basic Setting
5.2 Performance Evaluations
6 Conclusion
References
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder
1 Introduction
2 Related Works
3 Visually-Grounded Question Encoder (VGQE)
3.1 VGQE Cell
3.2 Using VGQE Cell to Encode the Question
3.3 Baseline VQA Architecture
4 Experiments and Results
4.1 Experimental Setup
4.2 Results
4.3 Performance of VGQE on Other Baselines
4.4 Performance of VGQE on the Standard VQAv2 Benchmark
5 Conclusion
References
Unsupervised Cross-Modal Alignment for Multi-person 3D Pose Estimation
1 Introduction
2 Related Work
3 Approaches
3.1 Architecture
3.2 Learning Cross-Modal Latent Space
3.3 Learning Beyond the Teacher Network
4 Experiments
4.1 Implementation Details
4.2 Ablation Studies
4.3 Datasets and Quantitative Evaluation
5 Discussion
6 Conclusion
References
Class-Incremental Domain Adaptation
1 Introduction
2 Background
3 Class-Incremental Domain Adaptation
3.1 Foresighted Source-Model Training
3.2 Class-Incremental DA on the Target Domain
4 Experiments
4.1 Discussion
5 Conclusion
References
Anti-bandit Neural Architecture Search for Model Defense
1 Introduction
2 Related Work
3 Anti-bandit Neural Architecture Search
3.1 Search Space
3.2 Adversarial Optimization for ABanditNAS
3.3 Anti-bandit
3.4 Anti-bandit Strategy for ABanditNAS
4 Experiments
4.1 Experiment Protocol
4.2 Results on Different Datasets
4.3 Ablation Study
5 Conclusion
References
Wavelet-Based Dual-Branch Network for Image Demoiréing
1 Introduction
2 Related Work
3 Our Method
3.1 Working in the Wavelet Domain
3.2 Dense Branch
3.3 Dilation Branch
3.4 Direction Perception Module
4 Experiments
4.1 Implementation Details
4.2 Datasets and State-of-the-Art Methods
4.3 Comparison with the State-of-the-Art
4.4 Ablation Studies
4.5 Extension to Deraining and Deraindrop
5 Conclusion
References
Low Light Video Enhancement Using Synthetic Data Produced with an Intermediate Domain Mapping
1 Introduction
2 Related Work
3 Learning the Low-Light Video RAW-to-RGB Mapping
3.1 Synthetic Data Generation Using an Intermediate Domain
3.2 Training Low-Light RAW-to-RGB Forward Models
3.3 GAN Architectures for Data Generation
4 Experimental Results
4.1 Datasets and Implementation Details
4.2 Synthetic Data Quality Evaluation
4.3 Output Image and Video Quality Evaluation
4.4 Real Training Data Quantity and Ratios
5 Conclusions
References
Non-local Spatial Propagation Network for Depth Completion
1 Introduction
2 Related Work
3 Non-local Spatial Propagation
3.1 Local Spatial Propagation Network
3.2 Non-local Spatial Propagation Network.
4 Confidence-Incorporated Affinity Learning
4.1 Affinity Normalization
4.2 Confidence-Incorporated Affinity Normalization
5 Depth Completion Network
5.1 Network Architecture
5.2 Loss Function
6 Experimental Results
6.1 NYU Depth V2
6.2 KITTI Depth Completion
6.3 Ablation Studies
7 Conclusion
References
DanbooRegion: An Illustration Region Dataset
1 Introduction
2 Background
3 The DanbooRegion Dataset
4 Feasibility-Based Assignment Recommendation
5 Benchmark
6 Application
7 Conclusion
References
Event Enhanced High-Quality Image Recovery
1 Introduction
2 Related Works
3 Problem Statement
3.1 Events and Intensity Images
3.2 Event Enhanced Degeneration Model
4 Event Enhanced High-Quality Image Recovery
4.1 Event-Enhanced Sparse Learning
4.2 Network
4.3 Network Training
5 High Frame-Rate Video Generation
6 Dataset Preparation
7 Experiments
7.1 Intensity Reconstruction Experiments
7.2 High Frame-Rate Video Experiments
8 Conclusion
References
PackDet: Packed Long-Head Object Detector
1 Introduction
2 Related Work
3 PackDet: Packed Long-Head Object Detector
3.1 Network Architecture
3.2 Packing Operator
3.3 Packed Long Head
3.4 Learning Targets
3.5 Loss Functions
3.6 Implementation Details
4 Experiments
4.1 Ablation Study
4.2 Comparison to State of the Art
5 Conclusions
References
A Generic Graph-Based Neural Architecture Encoding Scheme for Predictor-Based NAS
1 Introduction
2 Related Work
2.1 Architecture Evaluation Module
2.2 Architecture Searching Module
2.3 Neural Architecture Encoders
3 Method
3.1 Predictor-Based Neural Architecture Search
3.2 GATES: A Generic Neural Architecture Encoder
3.3 Neural Architecture Search Utilizing the Predictor
4 Experiments
4.1 Predictor Evaluation on NAS-Bench-101
4.2 Predictor Evaluation on NAS-Bench-201
4.3 Neural Architecture Search on NAS-Bench-101
4.4 Neural Architecture Search in the ENAS Search Space
5 Conclusion
References
Learning Semantic Neural Tree for Human Parsing
1 Introduction
2 Related Work
3 Methodology
3.1 Architecture
3.2 Loss Function
3.3 Handling Multiple Human Parsing
4 Experiment
4.1 Single Human Parsing
4.2 Multiple Human Parsing
4.3 Ablation Study
5 Conclusion
References
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation
1 Introduction
2 Related Works
3 Proposed Approach
3.1 Overview
3.2 Het Construction
3.3 Structured Context Encoding and Scene Graph Generation
3.4 Relation Ranking Module
3.5 Loss Function
4 Experimental Evaluation
4.1 Dataset, Evaluation and Settings
4.2 Ablation Studies
4.3 Comparisons with State-of-the-Arts
4.4 Analyses About het
5 Experiments on Image Captioning
6 Conclusion
References
Burst Denoising via Temporally Shifted Wavelet Transforms
1 Introduction
2 Background and Related Work
3 Methodology
3.1 Overview
3.2 Features Matter in Burst Denoising
3.3 Temporal Fusion of Deep Features
3.4 Loss Function
4 Data Preparation
4.1 Camera Pipeline
4.2 Datasets and Synthetic Burst Generation
5 Experimental Results
5.1 Overview
5.2 Ablation Study
5.3 Burst Denoising Qualitative Evaluation
5.4 Burst Denoising Quantitative Evaluation
5.5 Algorithm Efficiency
5.6 Limitations
5.7 Generalization to Real Burst Captures
6 Conclusion and Future Work
References
JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-modal Image Alignment of Large-Scale Pathological CT Scans
1 Introduction
2 Related Work
3 Methodology
3.1 Unpaired Image Synthesis
3.2 Multi-modal Image Registration
3.3 Multi-modal Image Segmentation
3.4 Joint Optimization Strategy
4 Experiments
4.1 Baseline
4.2 Implementation Details
4.3 Main Results
5 Ablation and Discussion
6 Conclusion
References
SimAug: Learning Robust Representations from Simulation for Trajectory Prediction
1 Introduction
2 Related Work
3 Approach
3.1 Problem Formulation
3.2 Training Data Generation from Simulation
3.3 Multi-view Simulation Augmentation (SimAug)
3.4 Backbone Model for Trajectory Prediction
4 Experiments
4.1 Evaluation Metrics
4.2 Main Results
4.3 State-of-the-Art Comparison on Stanford Drone Dataset
4.4 State-of-the-Art Comparison on VIRAT/ActEV
4.5 Ablation Experiments
5 Conclusion
References
ScribbleBox: Interactive Annotation Framework for Video Object Segmentation
1 Introduction
2 Related Work
3 Our Approach
3.1 Interactive Tracking Annotation
3.2 Interactive Segmentation Annotation
4 Experimental Results
4.1 In-Domain Annotation
4.2 Out-of-Domain Annotation
4.3 User Study
5 Conclusion
References
Rethinking Pseudo-LiDAR Representation
1 Introduction
2 Related Work
2.1 3D Detectors Based on Image Representation
2.2 3D Detectors Based on Pseudo-LiDAR Representation
3 Delving into Pseudo-LiDAR Representation
3.1 Review of Pseudo-LiDAR Based Detectors
3.2 PatchNet-Vanilla: Equivalent Implementation of Pseudo-LiDAR
3.3 Preliminary Conclusion
4 PatchNet
5 Experiments
5.1 Setup
5.2 Investigation of Pseudo-LiDAR Representation
5.3 Boosting the Performance of PatchNet
5.4 Comparing with State-of-the-Art Methods
5.5 Qualitative Results
6 Conclusions
References
Deep Multi Depth Panoramas for View Synthesis
1 Introduction
2 Related Work
3 Multi Depth Panoramas
4 Reconstructing and Rendering MDPs
4.1 Reconstructing Per-View MPIs from Images
4.2 Per-View MPIs to Per-View MDPs
4.3 Per-View MDP Blending
4.4 Differentiable MDP Rendering with Forward Splatting
5 Implementation Details
5.1 Dataset
5.2 Training
6 Results
7 Conclusion
References
MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection
1 Introduction
2 Related Work
3 Approach
3.1 Vision-Audio Fusion Module fF()
3.2 Highlight Estimation Module fE()
3.3 Bag Classification Module fC()
3.4 Objective Functions
4 Experiments
4.1 Datasets and Metrics
4.2 Compared Methods
4.3 Highlight Detection Results
4.4 Ablation Studies
5 Conclusion
References
ContactPose: A Dataset of Grasps with Object Contact and Hand Pose
1 Introduction
2 Related Work
3 The ContactPose Dataset
3.1 Data Capture Protocol and Equipment
3.2 Grasp Capture Without Hand Markers
4 Data Analysis
5 Contact Modeling Experiments
6 Results
7 Conclusion and Future Work
References
API-Net: Robust Generative Classifier via a Single Discriminator
1 Introduction
2 Related Work
3 The Proposed Method
3.1 Motivation
3.2 Anti-Perturbation Inference Net
3.3 Optimization
4 Experiments
4.1 Experimental Settings
4.2 Robustness
4.3 Ablation Study
5 Discussion and Conclusion
References
Bias-Based Universal Adversarial Patch Attack for Automatic Check-Out
1 Introduction
2 Related Work
2.1 Adversarial Attacks
2.2 Automatic Check-Out
3 Proposed Framework
3.1 Problem Definition
3.2 The Framework
3.3 Perceptually Biased Prior Generation
3.4 Training with Semantically Biased Prototypes
4 Experiments
4.1 Dataset and Evaluation Metrics
4.2 Experimental Settings
4.3 Digital-World Attack
4.4 Real-World Attack
4.5 Generalization Ability
4.6 Analysis of Textural Priors
4.7 Ablation Study
5 Conclusions
References
Imbalanced Continual Learning with Partitioning Reservoir Sampling
1 Introduction
2 Motivation: Fatal Forgetting on the Tail Classes
3 Approach
3.1 Problem Formulation
3.2 Conventional Reservoir Sampling
3.3 Fundamental Problems in Imbalanced Learning
3.4 Partitioning Reservoir Sampling
4 Related Work
5 The Multi-label Sequential Datasets
5.1 The COCOseq
5.2 The NUS-WIDEseq
6 Experiments
6.1 Experimental Design
6.2 Results
7 Conclusion
References
Guided Collaborative Training for Pixel-Wise Semi-Supervised Learning
1 Introduction
2 Related Work
2.1 SSL for Image Classification
2.2 SSL for Pixel-Wise Tasks
2.3 Prediction Confidence in SSL
2.4 Perturbations in SSL
3 Guided Collaborative Training
3.1 Overview of GCT
3.2 Flaw Detector
3.3 Dynamic Consistency Constraint
3.4 Flaw Correction Constraint
4 Experiments
4.1 Semantic Segmentation Experiments
4.2 Real Image Denoising Experiments
4.3 Portrait Image Matting Experiments
4.4 Night Image Enhancement Experiments
4.5 Ablation Experiments
5 Conclusions
References
Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework
1 Introduction
2 Related Work
2.1 Plug-and-Play Methods
2.2 Image Denoising
2.3 Non-uniform Blind Deblurring
3 Proposed Methods
3.1 Overview
3.2 Deep Plug-and-Play
3.3 Deep Prior
3.4 Adaptive Update Scheme
4 Experiments
4.1 Image Denoising
4.2 Image Deblurring
4.3 Analysis of Convergence
4.4 Mathematical Explanations
5 Conclusion
References
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight
1 Introduction
2 Related Work
2.1 Transfer Learning
2.2 Neural Architecture Search
3 Method
3.1 Problem Setting
3.2 Source Super-Network Training
3.3 Neural Architecture Search on Target
3.4 Neural Weight Search on Target
3.5 Generalization over Diverse Structures
4 Experiment
4.1 Objection Detection
4.2 Fine-Grained Classification
4.3 Semantic Segmentation
5 Conclusion
References
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation
1 Introduction
2 Related Works
3 Spatial Attention Pyramid Network
4 Experiment
4.1 Domain Adaptation for Detection
4.2 Domain Adaptation for Segmentation
4.3 Ablation Study
5 Conclusions
References
GSIR: Generalizable 3D Shape Interpretation and Reconstruction
1 Introduction
2 Related Work
3 Approach
3.1 Single-View Depth Estimation Module
3.2 Structure Interpretation Module
3.3 Structure-Guided Shape Completion Module
3.4 Voxel Refinement Module
3.5 Interpretation Consistency
3.6 Technical Details
4 Experiments
4.1 3D Shape Interpretation
4.2 Structure-Guided Shape Completion
4.3 3D Shape Reconstruction
4.4 Shape Interpretation with Consistency
4.5 Generalization to Real Images
4.6 Ablation Study
4.7 Shape Manipulation
5 Conclusion
References
Weakly Supervised 3D Object Detection from Lidar Point Cloud
1 Introduction
2 Related Work
3 Data Annotation Strategy for Our Weak Supervision
4 Proposed Algorithm
4.1 Learn to Generate Cylindrical Proposals from Click Annotations
4.2 Learn to Refine Proposals from a Few Well-Labeled Instances
4.3 Implementation Detail
5 Experiment
5.1 Experimental Setup
5.2 Quantitative and Qualitative Performance
5.3 Diagnostic Experiment
5.4 Performance as an Annotation Tool
6 Conclusion and Discussion
References
Two-Phase Pseudo Label Densification for Self-training Based Domain Adaptation
1 Introduction
2 Related Works
3 Preliminaries
3.1 Problem Setting
3.2 Self-training for UDA
3.3 Noisy Label Handling
4 Method
4.1 1st Phase: Voting Based Densification
4.2 2nd Phase: Easy-Hard Classification Based Densification
5 Experiments
5.1 Dataset
5.2 Implementation Details
5.3 Main Results
5.4 Ablation Study
5.5 Parameter Analysis
5.6 Loss Function Analysis
6 Conclusions
References
Adaptive Offline Quintuplet Loss for Image-Text Matching
1 Introduction
2 Related Work
3 Methods
3.1 Triplet Loss for Image-Text Matching
3.2 Offline Quintuplet Loss
3.3 Adaptive and Hierarchical Penalization
4 Experiments
4.1 Dataset and Experiment Settings
4.2 Implementation Details
4.3 Results on MS-COCO and Flickr30K
4.4 Ablation Study and Visualization
5 Conclusion
References
Learning Object Placement by Inpainting for Compositional Data Augmentation
1 Introduction
2 Related Work
2.1 Learning Object Placements
2.2 Data Augmentation for Object Detection
3 Methods
3.1 Data Acquisition by Inpainting
3.2 Learning Object Placements
3.3 Data Augmentation
4 Experiments
4.1 Baselines
4.2 Object Placements
4.3 Overfitting Inpainting Artifacts?
4.4 Data Augmentation for Object Detection
4.5 Data Augmentation for Instance Segmentation
4.6 Feature Representation Learning
5 Conclusion
References
Deep Vectorization of Technical Drawings
1 Introduction
2 Related Work
3 Our Vectorization System
3.1 Preprocessing of the Input Raster Image
3.2 Initial Estimation of Primitives
3.3 Refinement of the Estimated Primitives
3.4 Merging Estimations from All Patches
4 Experimental Evaluation
4.1 Clean Line Drawings
4.2 Degraded Line Drawings
4.3 Ablation Study
5 Conclusion
References
CAD-Deform: Deformable Fitting of CAD Models to 3D Scans
1 Introduction
2 Related Work
3 Overview of CAD-Deform Framework
4 Data-Driven Shape Deformation
4.1 Deformation Energy
4.2 Quadratic Terms
4.3 Data Term
4.4 Optimization
5 Datasets
6 Results
6.1 Evaluation Setup
6.2 Fitting Accuracy: How Well Do CAD Deformations Fit?
6.3 CAD Quality: How CAD-like Are Deformed Models?
6.4 Ablation Study
6.5 Shape Morphing Results
7 Conclusion
A Statistics on the Used Datasets
B Optimization Details
C Qualitative Fitting Results
D Morphing
E PartNet Annotation
F Fitting Accuracy Analysis
G Perceptual Assessment and User Study Details
References
An Image Enhancing Pattern-Based Sparsity for Real-Time Inference on Mobile Devices
1 Introduction
2 Background
3 Overview
4 Pattern Library - Theory and Design
4.1 A Unique Perspective on Weight Pruning
4.2 Pattern Library Design
5 Pattern-Aware Network Pruning Framework for Pattern Library Extraction
5.1 Pattern Library Extraction - A Single Step
5.2 Pattern Library Extraction - Overall
6 Connectivity Sparsity and the New Sparsity Induced Inference Framework
6.1 Connectivity Sparsity
6.2 Compiler-Assisted Inference Framework for Real-Time Execution
7 Experimental Results
7.1 Pattern Library Extraction Result
7.2 Visualization Demonstration and Accuracy Analysis for Pattern Pruning
7.3 Connectivity Pruning and Overall Model Compression Results
7.4 Performance Evaluation on Mobile Platform
8 Conclusion
References
AutoTrajectory: Label-Free Trajectory Extraction and Prediction from Videos Using Dynamic Points
1 Introduction
2 Related Work
2.1 Trajectory Prediction
2.2 Supervised Multi-object Tracking
2.3 Unsupervised Learning for Dynamic Modeling
3 Our Approach
3.1 Problem Definition
3.2 Method Overview
3.3 Dynamic-Point Modeling
3.4 Dynamic-to-Instance Aggregation
3.5 Instance Matching
3.6 Trajectory Prediction
3.7 Optimization
3.8 Network Architecture
4 Experiments
4.1 Implementation Details
4.2 Datasets
4.3 Results
4.4 Ablation Study
4.5 Limitations and Future Work
5 Conclusion
References
Multi-agent Embodied Question Answering in Interactive Environments
1 Introduction
2 Related Work
2.1 Question Answering in Embodied Environments
2.2 Multi-agent Systems
2.3 3D Computer Vision
2.4 Environments and Datasets
3 Overview of the Proposed Framework
4 Multi-agent 3D Reconstruction in Interactive Environments
4.1 Data Structure in Support of Interactive Environments
4.2 Structural Memory and Semantic Memory
4.3 Scanning Boundaries and Scanning Tasks
4.4 Viewpoint-Voxel Coverage Matrix
4.5 Termination Condition
4.6 Multi-agent 3D Reconstruction
5 Question Answering with 3D-CNN and LSTM
5.1 3D-CNN Scene Encoder and Question Encoder
5.2 Question Answering Model
5.3 Termination Model
5.4 Training the QA Model and the Termination Model
6 Experimental Results
6.1 Single-Agent IQA
6.2 Multi-agent IQA on IQuADv1 Dataset
6.3 Qualitative Examples
7 Conclusion
References
Conditional Sequential Modulation for Efficient Global Image Retouching
1 Introduction
2 Related Work
3 Method
3.1 Analysis of Retouching Operations
3.2 Conditional Sequential Retouching Network
4 Experiments
4.1 Comparison with State-of-the-Art Methods
4.2 Multiple Styles and Strength Control
4.3 Ablation Study
References
Segmenting Transparent Objects in the Wild*-1pc
1 Introduction
2 Related Work
3 Trans10K Dataset and Annotation
3.1 Data Description
3.2 Annotation
3.3 Dataset Complexity
3.4 Evaluation Metrics
4 Proposed Method
4.1 Network Architecture
4.2 Boundary Attention Module
4.3 Decoder
4.4 Loss Function
5 Experiments
5.1 Implementation Details
5.2 Ablation Studies
5.3 Comparison to the State-of-the-Arts
6 Conclusion
References
Length-Controllable Image Captioning
1 Introduction
2 Background and Related Works
2.1 Autoregressive Image Captioning (AIC)
2.2 Diverse and Controllable Image Captioning
2.3 Non-autoregressive Text Generation
3 Method
3.1 Acquisition of Length Information
3.2 Non-autoregressive Length-Controllable Decoding
4 Experiments
4.1 Dataset and Metrics
4.2 Implementation Details
4.3 Performance on AoANet and VLP
4.4 Performance on LaBERT
4.5 Performance Analysis of LaBERT
4.6 Controllability and Diversity Analysis
5 Conclusion
References
Few-Shot Semantic Segmentation with Democratic Attention Networks
1 Introduction
2 Related Work
2.1 Semantic Segmentation
2.2 Few-Shot Learning
2.3 Few-Shot Semantic Segmentation
3 Democratic Attention Network
3.1 Problem Definition
3.2 Architecture Overview
3.3 Democratized Graph Attention
3.4 Multi-scale Guidance
4 Experiments
4.1 PASCAL-5i
4.2 COCO-20i
4.3 FSS-1000
4.4 Ablation Study
5 Conclusion
References
Defocus Blur Detection via Depth Distillation
1 Introduction
2 Related Works
3 Methods
3.1 Depth Distillation
3.2 Network Structure
3.3 Loss Function
4 Experiments
4.1 Comparisons with State-of-the-Art Methods
4.2 Ablation Studies of Network Structure
4.3 Failure Cases
5 Conclusions
References
Motion Guided 3D Pose Estimation from Videos
1 Introduction
2 Related Work
3 Approach
3.1 Motion Loss
3.2 U-Shaped Graph Convolutional Networks
4 Experiments
4.1 Dataset
4.2 Evaluation Metric
4.3 Ablation Study
4.4 Comparison with State-of-the-art
5 Conclusion
References
Reflection Separation via Multi-bounce Polarization State Tracing
1 Introduction
2 Related Work
3 Physically-Based Image Formation Model
3.1 Polarization Image Formation Model
3.2 Polarization Simulation Engine
4 Proposed Method
4.1 Network Architecture
4.2 Perceptual and Simulation-Based Loss Function
5 Experiments
5.1 Datasets
5.2 Visual Comparison in Synthetic and Real Scene
5.3 Quantitative Evaluation
5.4 Ablation Study
6 Conclusion
References
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