{"type":"ScholarlyArticle","title":"ViSA-Gait: Leveraging Vision Foundation Models for Semantic Anchored Gait Recognition","authors":[{"name":"Xiangru Li","slug":"xiangru-li","given":"Xiangru","family":"Li","affiliation":""},{"name":"Deqiang Yin","slug":"deqiang-yin","given":"Deqiang","family":"Yin","affiliation":""},{"name":"Yifan Xie","slug":"yifan-xie","given":"Yifan","family":"Xie","affiliation":""},{"name":"Guojian Li","slug":"guojian-li","given":"Guojian","family":"Li","affiliation":""},{"name":"Zebang Cheng","slug":"zebang-cheng","given":"Zebang","family":"Cheng","affiliation":""},{"name":"Fei Ma","slug":"fei-ma","given":"Fei","family":"Ma","affiliation":""}],"abstract":"Gait recognition has achieved remarkable success in constrained environments, yet its performance often degrades significantly in cross-domain and cross-vertical-view scenarios. This is primarily due to the fact that domain-specific silhouette geometry causes models to overfit to extrinsic geometric characteristics rather than learning generalizable motion patterns. To address this issue, we present ViSA-Gait, which utilizes Vision Foundation Models (VFMs) as a “Semantic Compass” for stable universal human body knowledge guidance, enabling a lightweight backbone to robustly capture fine-grained gait dynamics. Specifically, we introduce a token-based distillation mechanism where a Spatial Token Learner (STL) and a Temporal Token Learner (TTL) filter dense VFM features into motion-consistent descriptors. These descriptors are then adaptively injected into a lightweight 3D-CNN backbone via a Gated Cross-Attention (GCA) mechanism, functioning as “Semantic Anchors” that regularize the feature space and guide the model to focus on intrinsic body motion. Extensive experiments demonstrate that ViSA-Gait achieves SOTA performance on cross-domain and cross-vertical-view benchmarks while remaining competitive within-domain, offering a new perspective on bridging the gap between geometry-based analysis and universal semantic understanding.","doi":"10.24963/ijcai.2026/151","url":"https://ijcai.org/proceedings/2026/151","pdf":"https://ijcai.org/proceedings/2026/0151.pdf","markdown":"https://ijcai.org/proceedings/2026/151.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/151","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":151,"pages":{"first":1351,"last":1359},"keywords":["Computer Vision: Recognition (object detection, categorization)","Computer Vision: Representation learning","Computer Vision: Low-level Vision","Computer Vision: Machine learning for vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}