Foreword......Page 5
Preface......Page 7
Contents......Page 9
Part I Motivation and Overview......Page 13
1.1.1 Perceptron......Page 14
1.1.2 Multilayer Perceptron......Page 15
1.1.3 Formulation of Neural Network......Page 17
1.2.1 Batch Normalization......Page 18
1.2.2 Batch Kalman Normalization......Page 20
References......Page 24
2.1 Face Detection......Page 25
2.2.1 Conventional Approaches......Page 26
2.2.2 Deep-Learning-Based Models......Page 27
2.3.1 Benchmarks for Pedestrian Detection......Page 28
2.3.2 Pedestrian Detection Methods......Page 29
References......Page 31
Part II Localizing Persons in Images......Page 36
3.1 Facial Landmark Machines......Page 38
3.2 The Cascaded BB-FCN Architecture......Page 40
3.2.1 Backbone Network......Page 41
3.2.2 Branch Network......Page 42
3.2.3 Ground Truth Heat Map Generation......Page 43
3.3.2 Evaluation Metric......Page 44
3.3.4 Comparison with the State of the Art......Page 45
3.4 Attention-Aware Face Hallucination......Page 46
3.4.1 The Framework of Attention-Aware Face Hallucination......Page 48
3.4.2 Recurrent Policy Network......Page 49
3.4.4 Deep Reinforcement Learning......Page 51
3.4.5 Experiments......Page 52
References......Page 53
4.1 Introduction......Page 55
4.2.1 Region Proposal Network for Pedestrian Detection......Page 57
4.2.3 Boosted Forest......Page 58
4.3 Experiments and Analysis......Page 59
References......Page 61
Part III Parsing Person in Detail......Page 63
5.1 Introduction......Page 66
5.2 Look into Person Benchmark......Page 68
5.3 Self-supervised Structure-Sensitive Learning......Page 69
5.3.1 Self-supervised Structure-Sensitive Loss......Page 71
5.3.2 Experimental Result......Page 73
References......Page 74
6.1 Introduction......Page 76
6.2 Related Work......Page 79
6.3 Crowd Instance-Level Human Parsing Dataset......Page 80
6.3.2 Dataset Statistics......Page 81
6.4 Part Grouping Network......Page 82
6.4.1 PGN Architecture......Page 83
6.4.2 Instance Partition Process......Page 85
6.5.1 Experimental Settings......Page 86
6.5.2 PASCAL-Person-Part Dataset......Page 87
6.5.4 Qualitative Results......Page 88
References......Page 89
7.1 Introduction......Page 91
7.2 Video Instance-Level Parsing Dataset......Page 92
7.3 Adaptive Temporal Encoding Network......Page 93
7.3.2 Parsing R-CNN......Page 96
7.3.3 Training and Inference......Page 97
References......Page 99
Part IV Identifying and Verifying Persons......Page 100
8.1 Introduction......Page 104
8.2 Generalized Similarity Measures......Page 106
8.2.1 Model Formulation......Page 109
8.2.2 Connection with Existing Models......Page 110
8.3.1 Deep Architecture......Page 111
8.3.2 Model Training......Page 113
8.4 Experiments......Page 116
References......Page 117
9.1 Introduction......Page 120
9.2 Related Work......Page 122
9.3 Framework Overview......Page 125
9.4 Formulation and Optimization......Page 126
References......Page 134
Part V Higher Level Tasks......Page 136
10.1 Introduction......Page 139
10.2 Deep Structured Model......Page 140
10.2.2 Latent Temporal Structure......Page 141
10.2.3 Deep Model with Relaxed Radius-Margin Bound......Page 143
10.3.1 Latent Temporal Structure......Page 146
10.3.2 Architecture of Deep Neural Networks......Page 147
10.4 Learning Algorithm......Page 149
10.4.1 Joint Component Learning......Page 150
10.4.3 Inference......Page 153
10.5.1 Datasets and Setting......Page 154
10.5.2 Empirical Analysis......Page 155
References......Page 159