Although gait recognition has drawn increasing research attention recently,
it remains challenging to learn discriminative temporal representation, since
the silhouette differences are quite subtle in spatial domain. Inspired by the
observation that human can distinguish gaits of different subjects by
adaptively focusing on temporal clips with different time scales, we propose a
context-sensitive temporal feature learning (CSTL) network for gait
recognition. CSTL produces temporal features in three scales, and adaptively
aggregates them according to the contextual information from local and global
perspectives. Specifically, CSTL contains an adaptive temporal aggregation
module that subsequently performs local relation modeling and global relation
modeling to fuse the multi-scale features. Besides, in order to remedy the
spatial feature corruption caused by temporal operations, CSTL incorporates a
salient spatial feature learning (SSFL) module to select groups of
discriminative spatial features. Particularly, we utilize transformers to
implement the global relation modeling and the SSFL module. To the best of our
knowledge, this is the first work that adopts transformer in gait recognition.
Extensive experiments conducted on three datasets demonstrate the
state-of-the-art performance. Concretely, we achieve rank-1 accuracies of
98.7%, 96.2% and 88.7% under normal-walking, bag-carrying and coat-wearing
conditions on CASIA-B, 97.5% on OU-MVLP and 50.6% on GREW.

本论文提出了一种上下文敏感的时态特征学习（CSTL）网络，用于步态识别，其中在三个尺度上生成时间特征，并根据来自局部和全局视角的上下文信息自适应地聚合它们。通过使用 transformer 实现全局关系建模和选取差异化空间特征的策略，显著提高了步态识别的准确性。