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Table of contents
1 introduction
1.1 Context
1.1.1 Scientific Context
1.1.2 Industrial Context
1.2 Visual Understanding Framework
1.2.1 Statistical Supervised Learning Framework
1.2.2 Image Classification
1.2.3 Object Detection
1.2.4 Weakly Supervised Learning
1.2.5 Multi-Task Learning
1.3 Motivations
1.4 Contributions and Outline
1.5 Related Publications
2 learning localized representations from image-level supervision
2.1 Introduction
2.2 Related Work
2.3 WILDCAT Model
2.3.1 Fully Convolutional Architecture
2.3.2 Multi-Map Transfer Layer
2.3.3 WILDCAT Pooling
2.3.4 WILDCAT Applications
2.4 Classification Experiments
2.4.1 Comparison with State of the Art
2.4.2 Further Analysis
2.5 Weakly Supervised Experiments
2.5.1 Weakly Supervised Pointwise Object Localization
2.5.2 Weakly Supervised Semantic Segmentation
2.5.3 Visualization of Results
2.6 Conclusion
3 learning part-based representations from object-level supervision
3.1 Introduction
3.2 Related Work
3.3 Deformable Part-based Fully Convolutional Networks
3.3.1 Fully Convolutional Feature Extractor
3.3.2 Deformable Part-based RoI Pooling
3.3.3 Classification and Localization Predictions with Deformable Parts
3.4 Experiments
3.4.1 Main Results
3.4.2 Ablation Study
3.4.3 Further Analysis
3.4.4 Comparison with State of the Art
3.4.5 Examples of Detections
3.5 Conclusion
4 learning task-related representations from auxiliary supervisions
4.1 Introduction
4.2 Related Work
4.3 ROCK: Residual Auxiliary Block
4.3.1 Merging of Primary and Auxiliary Representations
4.3.2 Effective MTL from Auxiliary Supervision
4.4 Application to Object Detection with Multi-Modal Auxiliary Information
4.5 Experiments
4.5.1 Ablation Study
4.5.2 Comparison with State of the Art
4.5.3 Pre-Training on Large-Scale MLT Dataset
4.5.4 Further Analysis
4.6 Conclusion
5 conclusion
5.1 Summary of Contributions
5.2 Perspectives for Future Work
