Learning Effective Multi-modal Trackers via Modality-Sensitive Tuning
Keywords: Multi-modal Tracking; Cross-modal Fine-Tuning
Abstract: This paper tackles the critical issue of constructing multi-modal trackers by effectively adapting the extensive knowledge of pre-trained RGB trackers to auxiliary modalities.To address the challenges, we propose a novel modality sensitivity-aware tuning framework, namely MST, which delicately models the learning process via adaptive tuning of model weights by inherent modality characteristics. Specifically, we first investigate the parameter modality-sensitivity as a criterion for measuring a precise element-wise essentiality for multi-modal adaptation. Then, in the tuning phase, we further leverage such sensitivity to bolster the stability and coherence of multi-modal representations, thereby enhancing generalization capabilities. Extensive experiments showcase the effectiveness of the proposed method, surpassing current state-of-the-art techniques across various multi-modal tracking scenarios and demonstrating remarkable performance even in extreme conditions. The source code will be publicly available.
Supplementary Material: zip
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 3465
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