Saihui Hou

Gait attribute recognition now has a large multimodal benchmark: RA-GAR covers 1,001 subjects and 242,729 walking sequences annotated with 15 static and motion gait attributes, addressing the shortage of fine-grained annotation. The accompanying Gait-VL matches attribute-specific multi-frame visual evidence against textual class prototypes instead of representing candidate classes through learned classifier output weights. It reaches state-of-the-art attribute recognition on RA-GAR, and replacing generic upstream vision models with the attribute-pretrained encoder yields consistent gains across several identity recognition pipelines and benchmarks.

Gait attribute recognition now has a large multimodal benchmark: RA-GAR covers 1,001 subjects and 242,729 walking sequences annotated with 15 static and motion gait attributes, addressing the shortage of fine-grained annotation. The accompanying Gait-VL matches attribute-specific multi-frame visual evidence against textual class prototypes instead of representing candidate classes through learned classifier output weights. It reaches state-of-the-art attribute recognition on RA-GAR, and replacing generic upstream vision models with the attribute-pretrained encoder yields consistent gains across several identity recognition pipelines and benchmarks.