Abstract
Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industrial communities. However, existing gait recognition methods heavily rely on task-specific upstream driven by supervised learning to provide explicit gait representations, which inevitably introduce expensive annotation costs and potentially cause cumulative errors. Escaping from this trend, this work explores effective gait representations based on the all-purpose knowledge produced by task-agnostic Large Vision Models (LVMs) and proposes a simple yet efficient gait framework, termed BigGait. Specifically, the Gait Representation Extractor (GRE) in BigGait effectively transforms all-purpose knowledge into implicit gait features in an unsupervised manner, drawing from design principles of established gait representation construction approaches. Experimental results on CCPG, CAISA-B* and SUSTech1K indicate that BigGait significantly outperforms the previous methods in both self-domain and cross-domain tasks in most cases, and provides a more practical paradigm for learning the next-generation gait representation. Eventually, we delve into prospective challenges and promising directions in LVMs-based gait recognition, aiming to inspire future work in this emerging topic. The source code will be available at this https URL.
Abstract (translated)
翻译： Gait识别是远程识别技术中最关键的一个，并随着研究和工业社区的不断发展而扩展。然而，现有的gait识别方法在很大程度上依赖于任务特定的上游驱动的监督学习来提供明确的gait表示，这无疑会带来昂贵的注释成本，并可能导致累积错误。为了摆脱这一趋势，本文探讨了基于任务无关的大型视觉模型（LVMs）产生的全知论的有效gait表示，并提出了一个简单而有效的gait框架，称为BigGait。具体来说，BigGait中的Gait表示提取器（GRE）以非监督方式将全知论转换为隐含的gait特征，并从现有gait表示构建方法的设计原则中汲取了设计原则。在CCPG、CAISA-B*和SUSTech1K等实验中，BigGait在自领域和跨领域任务中的表现明显优于以前的方法，并为学习下一代的gait表示提供了一个更实际的范例。最后，我们深入探讨了基于LVMs的gait识别中的潜在挑战和有前景的 direction，旨在激发未来在这个新兴领域的研究工作。源代码将在此链接中提供。
URL
https://arxiv.org/abs/2402.19122
