Abstract
Gait recognition is emerging as a promising technology and an innovative field within computer vision. However, existing methods typically rely on complex architectures to directly extract features from images and apply pooling operations to obtain sequence-level representations. Such designs often lead to overfitting on static noise (e.g., clothing), while failing to effectively capture dynamic motion this http URL address the above challenges, we present a Language guided and Motion-aware gait recognition framework, named this http URL particular, we utilize designed gait-related language cues to capture key motion features in gait sequences.
Abstract (translated)
步态识别正逐渐成为计算机视觉领域中一项有前景的技术和创新领域。然而，现有的方法通常依赖于复杂的架构直接从图像中提取特征，并应用池化操作来获取序列级别的表示。这样的设计往往会导致过度拟合静态噪声（例如服装），而无法有效捕捉动态运动信息。为了解决上述挑战，我们提出了一种语言引导且注重运动的步态识别框架，命名为LangMoGR。 具体而言，我们利用了专门设计的步态相关语言线索来捕捉步态序列中的关键运动特征。
URL
https://arxiv.org/abs/2601.11931
