Generalized Inter-class Loss for Gait Recognition | |
Yu, Weichen; Yu, Hongyuan; Huang, Yan; Wang, Liang | |
2022-05 | |
会议日期 | 2022-5 |
会议地点 | Lisboa, Portugal |
英文摘要 | Gait recognition is a unique biometric technique that can be performed at a long distance non-cooperatively and has broad applications in public safety and intelligent traffic systems. Previous gait works focus more on minimizing the intra-class variance while ignoring the significance in constraining inter-class variance. To this end, we propose a generalized inter-class loss which resolves the inter-class variance from both sample-level feature distribution and class-level feature distribution. Instead of equal penalty strength on pair scores, the proposed loss optimizes sample-level inter-class feature distribution by dynamically adjusting the pairwise weight. Further, in class-level distribution, generalized interclass loss adds a constraint on the uniformity of inter-class feature distribution, which forces the feature representations to approximate a hypersphere and keep maximal inter-class variance. In addition, the proposed method automatically adjusts the margin between classes which enables the inter-class feature distribution to be more flexible. The proposed method can be generalized to different gait recognition networks and achieves significant improvements. We conduct a series of experiments on CASIA-B and OUMVLP, and the experimental results show that the proposed loss can significantly improve the performance and achieves the state-of-the-art performances. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/52297] ![]() |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Wang, Liang |
作者单位 | 中国科学院自动化研究所, |
推荐引用方式 GB/T 7714 | Yu, Weichen,Yu, Hongyuan,Huang, Yan,et al. Generalized Inter-class Loss for Gait Recognition[C]. 见:. Lisboa, Portugal. 2022-5. |
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