Rethinking Global Context in Crowd Counting
文献类型：期刊论文
| 作者 | Guolei Sun1; Yun Liu2; Thomas Probst3; Danda Pani Paudel1; Nikola Popovic1; Luc Van Gool1 |
| 刊名 | Machine Intelligence Research
 |
| 出版日期 | 2024 |
| 卷号 | 21期号:4页码:640-651 |
| 关键词 | Crowd counting vision transformer global context attention density map |
| ISSN号 | 2731-538X |
| DOI | 10.1007/s11633-023-1475-z |
| 英文摘要 | This paper investigates the role of global context for crowd counting. Specifically, a pure transformer is used to extract features with global information from overlapping image patches. Inspired by classification, we add a context token to the input sequence, to facilitate information exchange with tokens corresponding to image patches throughout transformer layers. Due to the fact that trans formers do not explicitly model the tried-and-true channel-wise interactions, we propose a token-attention module (TAM) to recalibrate encoded features through channel-wise attention informed by the context token. Beyond that, it is adopted to predict the total person count of the image through regression-token module (RTM). Extensive experiments on various datasets, including ShanghaiTech, UCF QNRF, JHU-CROWD++ and NWPU, demonstrate that the proposed context extraction techniques can significantly improve the per formance over the baselines. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/58564]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.Computer Vision Lab, ETH Zürich, Zürich 8092, Switzerland 2.Institute for Infocomm Research, A*STAR, Singapore 138632, Singapore 3.Magic Leap, Zürich 8050, Switzerland |
| 推荐引用方式 GB/T 7714 | Guolei Sun, Yun Liu, Thomas Probst,et al. Rethinking Global Context in Crowd Counting[J]. Machine Intelligence Research,2024,21(4):640-651. |
| APA | Guolei Sun, Yun Liu, Thomas Probst, Danda Pani Paudel,Nikola Popovic,& Luc Van Gool.(2024).Rethinking Global Context in Crowd Counting.Machine Intelligence Research,21(4),640-651. |
| MLA | Guolei Sun,et al."Rethinking Global Context in Crowd Counting".Machine Intelligence Research 21.4(2024):640-651. |
入库方式： OAI收割
来源：自动化研究所
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