Learning to Track Instance from Single Nature Language Description
How to achieve vision-language (VL) tracking using natural language descriptions from a video sequence \textbf{without relying on any bounding-box ground truth}? In this work, we achieve this goal by tackling \textit{self-supervised VL tracking}, which aims to evaluate tracking capabilities guided by natural language descriptions. We introduce \textbf{\tracker}, a novel self-supervised VL tracker that is capable of tracking any referred object by a language description. Unlike traditional methods that equally fuse all language and visual tokens, we propose an efficient Dynamic Token Aggregation Module, which treats each visual token \textbf{unequally}. The module consists of three main st...
| Authors | Yaozong Zheng, Bineng Zhong, Qihua Liang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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