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Table of contents
1 introduction
1.1 Context
1.2 Computer vision, image and video understanding
1.3 Extending computer vision using semantic
1.4 Contribution
1.5 Industrial context
2 literature review
2.1 Statistical learning
2.1.1 Supervised learning
2.1.2 Loss functions
2.1.3 Neural networks
2.2 Mono-modal representation
2.2.1 Computer vision and image representation
2.2.2 Computer vision datasets
2.2.3 Natural language processing and text representation
2.3 Multi-modal representation
2.3.1 Multimodal fusion
2.3.2 Visual semantic embeddings
2.4 Attention mechanism
2.5 Localization
2.6 Positionning
3 visual semantic embedding
3.1 Introduction
3.2 Visual Semantic Embedding
3.2.1 Textual path
3.2.2 BEAN Visual path
3.2.3 SMILE visual path
3.2.4 Learning and loss function
3.2.5 Re-ranking
3.3 Retrieval experiments
3.3.1 Training
3.3.2 Cross-modal retrieval
3.3.3 Discussion
3.4 Ablation and model understanding
3.4.1 BEAN: Changing pooling
3.4.2 SMILE: Impact of self-attention
3.4.3 Further analysis
3.5 Conclusion
4 application to localization
4.1 Introduction
4.2 Localization from visual semantic embedding
4.2.1 BEAN: Weakly supervised localization
4.2.2 SMILE: Object region to localization using Visual Semantic Embedding (VSE)
4.3 Experiments
4.3.1 The pointing game
4.3.2 Further analysis
4.4 Conclusion
5 ranking loss function
5.1 Introduction
5.2 Related works
5.3 SoDeep approach
5.3.1 Learning a sorting proxy
5.3.2 SoDeep Training and Analysis
5.4 Differentiable Sorter based loss functions
5.4.1 Spearman correlation
5.4.2 Mean Average Precision (mAP)
5.4.3 Recall at K
5.5 Experimental Results
5.5.1 Spearman Correlation: Predicting Media Memorability
5.5.2 Mean Average precision: Image classification
5.5.3 Recall@K: Cross-modal Retrieval
5.6 Discussion
5.7 Conclusion
6 general conclusion
6.1 Summary of contributions
6.2 Perspectives and future work
bibliography
