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Erschienen in:
Spatio-Temporally Consistent Correspondence for Dense Dynamic Scene Modeling Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Human Attribute Recognition by Deep Hierarchical Contexts

verfasst von : Yining Li, Chen Huang, Chen Change Loy, Xiaoou Tang

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

We present an approach for recognizing human attributes in unconstrained settings. We train a Convolutional Neural Network (CNN) to select the most attribute-descriptive human parts from all poselet detections, and combine them with the whole body as a pose-normalized deep representation. We further improve by using deep hierarchical contexts ranging from human-centric level to scene level. Human-centric context captures human relations, which we compute from the nearest neighbor parts of other people on a pyramid of CNN feature maps. The matched parts are then average pooled and they act as a similarity regularization. To utilize the scene context, we re-score human-centric predictions by the global scene classification score jointly learned in our CNN, yielding final scene-aware predictions. To facilitate our study, a large-scale WIDER Attribute dataset(Dataset URL: http://​mmlab.​ie.​cuhk.​edu.​hk/​projects/​WIDERAttribute) is introduced with human attribute and image event annotations, and our method surpasses competitive baselines on this dataset and other popular ones.

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Vorheriges Kapitel Semantic 3D Reconstruction of Heads
Nächstes Kapitel Person Re-identification via Recurrent Feature Aggregation
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Fußnoten
1
The term ‘Hierarchical Context’ is used in this paper to denote the tree-structured organization of object classes in a scene. We use the same term but with a different meaning of (human) object-object and object-scene contextual relations at two semantic levels, which is also more complete in the coverage of image information.
 
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Metadaten
Titel
Human Attribute Recognition by Deep Hierarchical Contexts
verfasst von
Yining Li
Chen Huang
Chen Change Loy
Xiaoou Tang
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46465-7

Electronic ISBN: 978-3-319-46466-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46466-4_41

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