Human attribute recognition by deep hierarchical contexts
Refereed conference paper presented and published in conference proceedings

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其它資訊
摘要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 largescale 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.
著者Li Y., Huang C., Loy C.C., Tang X.
會議名稱European Conference on Computer Vision
會議開始日08.10.2016
會議完結日16.10.2016
會議地點Amsterdam
會議國家/地區荷蘭
會議論文集題名ECCV 2016: Computer Vision – ECCV 2016
出版年份2016
卷號9910 LNCS
出版社Springer Verlag
出版地Germany
頁次684 - 700
國際標準書號9783319464657
國際標準期刊號1611-3349
語言英式英語

上次更新時間 2020-09-08 於 04:37