Botom-Up Foreground-Aware Feature Fusion for Person Search | |
Yang, Wenjie2,3,4; Li, Dangwei2,3,4; Chen, Xiaotang2,3,4; Huang, Kaiqi1,2,3,4 | |
2020-10 | |
会议日期 | 12-16 October 2020 |
会议地点 | Seattle, United States |
英文摘要 | The key to efcient person search is jointly localizing pedestrians and learning discriminative representation for person re-identifcation (re-ID). Some recently developed task-joint models are built with separate detection and re-ID branches on top of shared region feature extraction networks, where the large receptive feld of neurons leads to background information redundancy for the following re-ID task. Our diagnostic analysis indicates the task-joint model suffers from considerable performance drop when the background is replaced or removed. In this work, we propose a subnet to fuse the bounding box features that pooled from multiple ConvNet stages in a bottom-up manner, termed bottom-up fusion (BUF) network. With a few parameters introduced, BUF leverages the multi-level features with different sizes of receptive felds to mitigate the backgroundbias problem. Moreover, the newly introduced segmentation head generates a foreground probability map as guidance for the network to focus on the foreground regions. The resulting foreground attention module (FAM) enhances the foreground features. Extensive experiments on PRW and CUHK-SYSU validate the effectiveness of the proposals. Our Bottom-Up Foreground-Aware Feature Fusion (BUFF) network achieves considerable gains over the state-of-thearts on PRW and competitive performance on CUHK-SYSU. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44901] |
专题 | 智能系统与工程 |
通讯作者 | Huang, Kaiqi |
作者单位 | 1.CAS Center for Excellence in Brain Science and Intelligence Technology 2.Center for Research on Intelligent System and Engineering 3.University of Chinese Academy of Sciences 4.Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Yang, Wenjie,Li, Dangwei,Chen, Xiaotang,et al. Botom-Up Foreground-Aware Feature Fusion for Person Search[C]. 见:. Seattle, United States. 12-16 October 2020. |
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