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Table of Contents
Preface; Contents; 1 Introduction to Visual Attributes; 1.1 Overview of the Chapters; References; Part I Attribute-Based Recognition; 2 An Embarrassingly Simple Approach to Zero-Shot Learning; 2.1 Introduction; 2.2 Related Work; 2.3 Embarrassingly Simple ZSL ; 2.3.1 Regularisation and Loss Function Choices ; 2.4 Risk Bounds; 2.4.1 Simple ZSL as a Domain Adaptation Problem ; 2.4.2 Risk Bounds for Domain Adaptation ; 2.5 Experiments; 2.5.1 Synthetic Experiments; 2.5.2 Real Data Experiments; 2.6 Discussion; References.
3 In the Era of Deep Convolutional Features: Are Attributes Still Useful Privileged Data?3.1 Introduction; 3.2 Related Work; 3.3 Learning Using Privileged Information; 3.3.1 Maximum-Margin Model 1: SVM+; 3.3.2 Maximum-Margin Model 2: Margin Transfer; 3.3.3 How Is Information Being Transferred?; 3.4 Experiments; 3.4.1 Object Recognition in Images; 3.4.2 Recognizing Easy from Hard Images; 3.5 Conclusion; References; 4 Divide, Share, and Conquer: Multi-task Attribute Learning with Selective Sharing; 4.1 Introduction; 4.2 Learning Decorrelated Attributes; 4.2.1 Approach.
4.2.2 Experiments and Results4.3 Learning Analogous Category-Sensitive Attributes; 4.3.1 Approach; 4.3.2 Experiments and Results; 4.4 Related Work; 4.4.1 Attributes as Semantic Features; 4.4.2 Attribute Correlations; 4.4.3 Differentiating Attributes; 4.4.4 Multi-task Learning (MTL); 4.5 Conclusion; References; Part II Relative Attributes and Their Application to Image Search; 5 Attributes for Image Retrieval; 5.1 Introduction; 5.2 Comparative Relevance Feedback Using Attributes; 5.2.1 Learning to Predict Relative Attributes; 5.2.2 Relative Attribute Feedback; 5.2.3 Experimental Validation.
5.3 Actively Guiding the User's Relevance Feedback5.3.1 Attribute Binary Search Trees; 5.3.2 Predicting the Relevance of an Image; 5.3.3 Actively Selecting an Informative Comparison; 5.3.4 Experimental Validation; 5.4 Accounting for Differing User Perceptions of Attributes; 5.4.1 Adapting Attributes; 5.4.2 Experimental Validation; 5.5 Discovering Attribute Shades of Meaning; 5.5.1 Collecting Personal Labels and Label Explanations; 5.5.2 Discovering Schools and Training Per-School Adapted Models; 5.5.3 Experimental Validation; 5.6 Discussion and Conclusion; References.
6 Fine-Grained Comparisons with Attributes6.1 Introduction; 6.2 Related Work; 6.3 Ranking Functions for Relative Attributes; 6.4 Fine-Grained Visual Comparisons; 6.4.1 Local Learning for Visual Comparisons; 6.4.2 Selecting Fine-Grained Neighboring Pairs; 6.4.3 Fine-Grained Attribute Zappos Dataset; 6.4.4 Experiments and Results; 6.4.5 Predicting Useful Neighborhoods; 6.5 Just Noticeable Differences; 6.5.1 Local Bayesian Model of Distinguishability; 6.5.2 Experiments and Results; 6.6 Discussion; 6.7 Conclusion; References; 7 Localizing and Visualizing Relative Attributes; 7.1 Introduction.
3 In the Era of Deep Convolutional Features: Are Attributes Still Useful Privileged Data?3.1 Introduction; 3.2 Related Work; 3.3 Learning Using Privileged Information; 3.3.1 Maximum-Margin Model 1: SVM+; 3.3.2 Maximum-Margin Model 2: Margin Transfer; 3.3.3 How Is Information Being Transferred?; 3.4 Experiments; 3.4.1 Object Recognition in Images; 3.4.2 Recognizing Easy from Hard Images; 3.5 Conclusion; References; 4 Divide, Share, and Conquer: Multi-task Attribute Learning with Selective Sharing; 4.1 Introduction; 4.2 Learning Decorrelated Attributes; 4.2.1 Approach.
4.2.2 Experiments and Results4.3 Learning Analogous Category-Sensitive Attributes; 4.3.1 Approach; 4.3.2 Experiments and Results; 4.4 Related Work; 4.4.1 Attributes as Semantic Features; 4.4.2 Attribute Correlations; 4.4.3 Differentiating Attributes; 4.4.4 Multi-task Learning (MTL); 4.5 Conclusion; References; Part II Relative Attributes and Their Application to Image Search; 5 Attributes for Image Retrieval; 5.1 Introduction; 5.2 Comparative Relevance Feedback Using Attributes; 5.2.1 Learning to Predict Relative Attributes; 5.2.2 Relative Attribute Feedback; 5.2.3 Experimental Validation.
5.3 Actively Guiding the User's Relevance Feedback5.3.1 Attribute Binary Search Trees; 5.3.2 Predicting the Relevance of an Image; 5.3.3 Actively Selecting an Informative Comparison; 5.3.4 Experimental Validation; 5.4 Accounting for Differing User Perceptions of Attributes; 5.4.1 Adapting Attributes; 5.4.2 Experimental Validation; 5.5 Discovering Attribute Shades of Meaning; 5.5.1 Collecting Personal Labels and Label Explanations; 5.5.2 Discovering Schools and Training Per-School Adapted Models; 5.5.3 Experimental Validation; 5.6 Discussion and Conclusion; References.
6 Fine-Grained Comparisons with Attributes6.1 Introduction; 6.2 Related Work; 6.3 Ranking Functions for Relative Attributes; 6.4 Fine-Grained Visual Comparisons; 6.4.1 Local Learning for Visual Comparisons; 6.4.2 Selecting Fine-Grained Neighboring Pairs; 6.4.3 Fine-Grained Attribute Zappos Dataset; 6.4.4 Experiments and Results; 6.4.5 Predicting Useful Neighborhoods; 6.5 Just Noticeable Differences; 6.5.1 Local Bayesian Model of Distinguishability; 6.5.2 Experiments and Results; 6.6 Discussion; 6.7 Conclusion; References; 7 Localizing and Visualizing Relative Attributes; 7.1 Introduction.