CASIA OpenIR
Pedestrian Attribute Recognition by Joint Visual-semantic Reasoning and Knowledge Distillation
Li QZ(李乔哲); Zhao X(赵鑫); He R(赫然); Huang KQ(黄凯奇)
2019-08
Conference Name28th International Joint Conference on Artificial Intelligence
Conference Date2019-8
Conference Place中国澳门
Abstract

Pedestrian attribute recognition in surveillance is a challenging task in computer vision due to significant pose variation, viewpoint change and poor image quality. To achieve effective recognition, this paper presents a graph-based global reasoning framework to jointly model potential visual-semantic relations of attributes and distill auxiliary human parsing knowledge to guide the relational learning. The reasoning framework models attribute groups on a graph and learns a projection function to adaptively assign local visual features to the nodes of the graph. After feature projection, graph convolution is utilized to perform global reasoning between the attribute groups to model their mutual dependencies. Then, the learned node features are projected back to visual space to facilitate knowledge transfer. An additional regularization term is proposed by distilling human parsing knowledge from a pre-trained teacher model to enhance feature representations. The proposed framework is verified on three large scale pedestrian attribute datasets including PETA, RAP, and PA100k. Experiments show that our method achieves state-of-the-art results.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28373
Collection中国科学院自动化研究所
Corresponding AuthorHuang KQ(黄凯奇)
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li QZ,Zhao X,He R,et al. Pedestrian Attribute Recognition by Joint Visual-semantic Reasoning and Knowledge Distillation[C],2019.
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