Knowledge Commons of Institute of Automation,CAS
Pedestrian Attribute Recognition by Joint Visual-semantic Reasoning and Knowledge Distillation | |
Li, Qiaozhe![]() ![]() ![]() ![]() | |
2019-08 | |
会议名称 | 28th International Joint Conference on Artificial Intelligence |
会议日期 | 2019-8 |
会议地点 | 中国澳门 |
摘要 | 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. |
收录类别 | EI |
七大方向——子方向分类 | 图像视频处理与分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/28373 |
专题 | 复杂系统认知与决策实验室_智能系统与工程 |
通讯作者 | Huang, Kaiqi |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li, Qiaozhe,Zhao, Xin,He, Ran,et al. Pedestrian Attribute Recognition by Joint Visual-semantic Reasoning and Knowledge Distillation[C],2019. |
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0117.pdf(640KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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