BigGait: Learning Gait Representation You Want by Large Vision Models
CVPR 2024(2024)
摘要
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 https://github.com/ShiqiYu/OpenGait.
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