Proceedings

Joint Neural Architecture Search and Token Pruning for Efficient Visual Tracking

Yihong Chen, Shuo Wang, Jiayao Zheng, Yongqiang Bai

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 1007-1015.

https://doi.org/10.24963/ijcai.2026/113

Keywords

  • Computer Vision: Motion and tracking
  • Computer Vision: Efficiency and Optimization

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.

Yihong Chen, Shuo Wang, Jiayao Zheng, and Yongqiang Bai. 2026. Joint Neural Architecture Search and Token Pruning for Efficient Visual Tracking. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 1007–1015. https://doi.org/10.24963/ijcai.2026/113
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