{"type":"ScholarlyArticle","title":"Joint Neural Architecture Search and Token Pruning for Efficient Visual Tracking","authors":[{"name":"Yihong Chen","slug":"yihong-chen","given":"Yihong","family":"Chen","affiliation":""},{"name":"Shuo Wang","slug":"shuo-wang","given":"Shuo","family":"Wang","affiliation":""},{"name":"Jiayao Zheng","slug":"jiayao-zheng","given":"Jiayao","family":"Zheng","affiliation":""},{"name":"Yongqiang Bai","slug":"yongqiang-bai","given":"Yongqiang","family":"Bai","affiliation":""}],"abstract":"Recently, transformer-based trackers have become the leading approach, surpassing traditional CNN-based trackers in accuracy. However, their high computational demands hinder their deployment on edge platforms, necessitating efficient solutions. To address this, we propose a novel Neural Architecture Search (NAS) framework designed for transformer-based trackers, named NASTrack. This framework incorporates token pruning to optimize both transformer block structures and the layer-wise token keeping ratio, striking a balance between performance and efficiency. To handle the larger search space introduced by the keeping ratio, we propose a blacklist strategy and a matching-based distillation driven by the Token Overlap Ratio (TOR). Our method discovers hundreds of high-performing trackers, with FLOPs ranging from 1G to 18G. The searched trackers consistently outperform existing efficient state-of-the-art trackers such as CompressTracker and LiteTrack under comparable computational budgets.The code and models are available at https://github.com/Cyhoon84/NASTrack.git.","doi":"10.24963/ijcai.2026/113","url":"https://ijcai.org/proceedings/2026/113","pdf":"https://ijcai.org/proceedings/2026/0113.pdf","markdown":"https://ijcai.org/proceedings/2026/113.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/113","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":113,"pages":{"first":1007,"last":1015},"keywords":["Computer Vision: Motion and tracking","Computer Vision: Efficiency and Optimization"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}