TrackMamba: Mamba-Transformer Tracking

Jiaming Zhang, Cheng Liang, Yutao Cui, Xiangbo Shu, Gangshan Wu, Limin Wang

Keywords: Mamba; Single Object Tracking

TL;DR: A mamba-transformer tracking framework is proposed for resolution-scalable visual tracking.

Abstract: Current one-stream Transformer-based trackers are quality but unfriendly to memory consumption of large resolution and long sequence, both of which are crucial keys to tracking tasks. Recently structured state space model (SSM) demonstrates promising performance and efficiency in sequence modeling but struggles to retrieve due to the limited hidden state number. To solve the computation challenge and explore the potential of Mamba, we propose TrackMamba, a Mamba-Transformer tracker containing TrackMamba Blocks and Attention Blocks. In order to better harness the scanning in TrackMamba Blocks for inter- and intra-frame modeling, we introduce various scan patterns for rearrangement and flipping. Furthermore, we propose Target Enhancement, including Temporal Token for target aggregation and search enhancement, and Temporal Mamba for target information cross-frame propagation. Extensive experiments show TrackMamba performs better than the first-generation one-stream Transformer-based tracker at same resolution and mitigates consumption growth when enlarging resolution, exhibiting the potential of Mamba-based model for large-resolution tracking.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 4593