A Swiss Army Knife for Tracking by Natural Language Specification

Kaige Mao, Xiaopeng Hong, Xiaopeng Fan, Wangmeng Zuo

Abstract: Tracking by natural language specification requires trackers to jointly perform grounding and tracking tasks. Existing methods either use separate models or a single shared network, failing to account for the link and diversity between tasks jointly. In this paper, we propose a novel framework that performs dynamic task switching to customize its network path routing for each task within a unified model. For this purpose, we design a task-switchable attention module, which enables the acquisition of modal relation patterns with different dominant modalities for each task via dynamic task switching. In addition, to alleviate the inconsistency between the static language description and the dynamic target appearance during tracking, we propose a language renovation mechanism that renovates the initial language online via visual-context-aware linguistic prompting. Extensive experimental results on five datasets demonstrate that the proposed method performs favorably against state-of-the-art approaches for both grounding and tracking. Our project will be available at: https://github.com/mkg1204/SAKTrack.