Bi-directional Interaction Network for Person Search | |
Dong, Wenkai2,3; Zhang, Zhaoxiang1,2,3; Song, Chunfeng2,3; Tan, Tieniu1,2,3 | |
2020 | |
会议日期 | June 16 - 18, 2020 |
会议地点 | Virtual Site |
英文摘要 | Existing works have designed end-to-end frameworks based on Faster-RCNN for person search. Due to the large receptive fields in deep networks, the feature maps of each proposal, cropped from the stem feature maps, involve redundant context information outside the bounding boxes. However, person search is a fine-grained task which needs accurate appearance information. Such context information can make the model fail to focus on persons, so the learned representations lack the capacity to discriminate various identities. To address this issue, we propose a Siamese network which owns an additional instance-aware branch, named Bi-directional Interaction Network (BINet). During the training phase, in addition to scene images, BINet also takes as inputs person patches which help the model discriminate identities based on human appearance. Moreover, two interaction losses are designed to achieve bi-directional interaction between branches at two levels. The interaction can help the model learn more discriminative features for persons in the scene. At the inference stage, only the major branch is applied, so BINet introduces no additional computation. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our BINet achieves state-of-the-art results among end-to-end methods without loss of efficiency. |
会议录出版者 | IEEE |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48647] |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Zhang, Zhaoxiang |
作者单位 | 1.Center for Excellence in Brain Science and Intelligence Technology, CAS 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Center for Research on Intelligent Perception and Computing, NLPR, CASIA |
推荐引用方式 GB/T 7714 | Dong, Wenkai,Zhang, Zhaoxiang,Song, Chunfeng,et al. Bi-directional Interaction Network for Person Search[C]. 见:. Virtual Site. June 16 - 18, 2020. |
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