Computer vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings. Part XXVIII / Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm (eds).

Intro
Foreword
Preface
Organization
Contents
Part XXVIII
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
1 Introduction
2 Related Work
2.1 Point-Cloud Segmentation
2.2 Adaptive Convolution
2.3 Efficient Neural Networks
3 Spherical Projection of LiDAR Point-Cloud
4 Spatially-Adaptive Convolution
4.1 Standard Convolution
4.2 Spatially-Adaptive Convolution
4.3 Efficient Computation of SAC
4.4 Relationship with Prior Work
5 SqueezeSegV3
5.1 The Architecture of SqueezeSegV3
5.2 Loss Function

6 Experiments
6.1 Dataset and Evaluation Metrics
6.2 Implementation Details
6.3 Comparing with Prior Methods
6.4 Ablation Study
7 Conclusion
References
An Attention-Driven Two-Stage Clustering Method for Unsupervised Person Re-identification
1 Introduction
2 Related Work
2.1 Unsupervised Person Re-ID
2.2 Attention in Person Re-ID
3 Our Approach
3.1 Voxel Attention (VA)
3.2 Two-Stage Clustering (TC)
3.3 Progressive Training
4 Experiments
4.1 Datasets
4.2 Implementation Details
4.3 Model Performances on Benchmark Datasets

4.4 Contribution of the Voxel Attention
4.5 Contribution of Two-Stage Clustering
4.6 Contribution of Progressive Training
4.7 Component Analysis of ADTC
5 Conclusion
References
Toward Fine-Grained Facial Expression Manipulation
1 Introduction
2 Related Work
3 Methodology
3.1 Relative Action Units (AUs)
3.2 Network Structure
3.3 Multi-scale Feature Fusion
3.4 Loss Functions
4 Experiments
4.1 Implementation Details
4.2 Evaluation Metrics
4.3 Qualitative Evaluation
4.4 Quantitative Evaluation
4.5 Ablation Study
5 Conclusion
References

Adaptive Object Detection with Dual Multi-label Prediction
1 Introduction
2 Related Work
3 Method
3.1 Multi-label Prediction
3.2 Conditional Adversarial Feature Alignment
3.3 Category Prediction Based Regularization
3.4 Overall End-to-End Learning
4 Experiments
4.1 Implementation Details
4.2 Domain Adaptation from Real to Virtual Scenes
4.3 Adaptation from Clear to Foggy Scenes
4.4 Ablation Study
4.5 Further Analysis
5 Conclusion
References
Table Structure Recognition Using Top-Down and Bottom-Up Cues
1 Introduction
2 Related Work

3 TabStruct-Net
3.1 Top-Down: Cell Detection
3.2 Bottom-Up: Structure Recognition
3.3 Post-Processing
4 Experiments
4.1 Datasets
4.2 Baseline Methods
4.3 Implementation Details
4.4 Evaluation Measures
4.5 Experimental Setup
5 Results on Table Structure Recognition
5.1 Analysis of Results
5.2 Ablation Study
6 Summary
References
Novel View Synthesis on Unpaired Data by Conditional Deformable Variational Auto-Encoder
1 Introduction
2 Related Works
3 Method
3.1 Overview Framework
3.2 Conditional Deformable Module (CDM)