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Table of contents
1 introduction
1.1 Context
1.2 Motivation
1.3 Contributions and Outline
1.4 Related Publications
2 related works
2.1 Traditional Methods
2.2 Fully Convolutional Network for Semantic Segmentation
2.2.1 Principle and Model
2.2.2 Learning Strategy
2.2.3 Dataset and Evaluation
2.2.4 Challenges
2.3 Optimizing Deep Architectures for Segmentation
2.3.1 Spatial Information
2.3.2 Contextual Information
2.4 Objective Function
2.5 Weakly-Supervised Segmentation
2.6 Positioning and Framework
3 semeda: enhancing segmentation precision with semantic edge aware loss
3.1 Introduction
3.2 PPCE Loss Extension
3.3 SEMEDA Model
3.3.1 The Pitfalls of Naive Segmentation Approaches
3.3.2 Structure Learning Through Edge Embeddings
3.3.3 Learning to Detect Semantic Edges
3.4 Experiments
3.4.1 Implementation Details
3.4.2 Experimental Setup
3.4.3 SEMEDA Parametrization
3.4.4 Quantitative Validation
3.4.5 Comparison with State-of-the-art Approaches
3.4.6 Qualitative Assessment
3.5 Applications
3.5.1 Face Parsing
3.5.2 Lesion Boundary Segmentation
3.6 Conclusion
4 weakly supervised segmentation with attribution methods
4.1 Introduction
4.2 Motivation and Context
4.3 Attribution for Convolutional Neural Networks
4.3.1 Related Works
4.3.2 Delving Deep into Interpreting Neural Nets with Piece-Wise Affine Representation
4.3.3 Experiment for Visual Explanation
4.4 Weakly Supervised Semantic Segmentation Using Attribution
4.4.1 Experiment Setup
4.4.2 Quantitative Results
4.4.3 Ablation Study
4.4.4 Discussion
4.5 Conclusion
5 conclusion
5.1 Summary of Contributions
5.2 Perspectives for Future Work
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