FMTrack: Frequency-Aware Interaction and Multi-Expert Fusion for RGB-T Tracking

Recently, RGB-T tracking has received increasing attention due to its robustness. However, existing RGB-T trackers mainly use cross-attention for modal feature interaction, limiting the utilization of complementary information. In addition, these trackers employ fixed dominant-auxiliary paradigms for feature fusion, ignoring modal quality fluctuations. To address these issues, we propose FMTrack, an effective framework for fully capturing complementary information. FMTrack consists of two key components, a frequency-aware interaction network (FIN) and a multi-expert fusion module (MEFM). To emphasize the valuable information in each modality, FIN utilizes frequency masks to perform high-pass and low-pass filtering on RGB and TIR data. FIN explicitly establishes cross-modal interactions via frequency domain learning, which facilitates the sharing of complementary information. Besides, MEFM extracts diverse features via the differentiated expert network and then adjusts feature combinations according to modal reliability, achieving deep understanding and flexible fusion of multimodal data. With FIN and MEFM, FMTrack makes full use of the advantageous information of each modality to highlight target representations, thus improving performance in complex scenes. Extensive experiments on four popular RGBT tracking datasets (LasHeR, VTUAV, RGBT234, and RGBT210) show that our FMTrack achieves leading performance. The code is available at https://github.com/xyl-507/FMTrack.