DKDTrack: dual-granularity knowledge distillation for RGBT tracking

RGBT tracking is an important research direction in the field of computer vision and has received increasing attention. It aims to exploit the complementary advantages between RGB and TIR modalities to achieve robust object tracking. However, existing studies usually focus on the fusion and interaction between modalities, ignoring the importance of learning modal representations. To address this issue, we propose a novel Dual-granularity Knowledge Distillation RGBT tracker named DKDTrack. In particular, the method introduces an adaptive distillation strategy to achieve representation enhancement between modalities by online measurement of modality strength and weakness relationships. In addition, we design a dual-granularity distillation module for jointly guiding the learning of weaker modalities at the feature level and the attention level. Extensive experiments on three publicly available RGBT datasets demonstrate the effectiveness of DKDTrack and highlight the importance of modal representation learning.