Pixel-Level RGBT Fusion Tracking via Heterogeneous Multi-Expert Distillation and Decoupled Representation Learning

Pixel-level fusion is widely considered a lightweight yet limited strategy in RGB-Thermal (RGBT) tracking due to its shallow representational capacity. However, its actual limitations and potential remain largely unexplored. We systematically analyze fusion location, modality alignment, and tracking performance, revealing that despite lower modality gaps than feature-level fusion, pixel-level fusion lacks task-relevant discrimination, restricting its effectiveness. In this paper, we propose the Task-driven Pixel-level Fusion tracker (TPF), which preserves the efficiency of early fusion while enhancing discriminative capacity. Central to TPF is a lightweight pixel fusion adapter that ensures real-time image fusion with only 14.3KB extra parameters over the baseline at inference. To enhance its limited representational capacity, we propose a task-driven progressive learning framework consisting of two key stages. First, a heterogeneous multi-expert distillation scheme adaptively transfers image fusion knowledge from diverse models under tracking-guided evaluation, mitigating the generalization limitations of single-teacher distillation across varied tracking scenarios. Second, to overcome limited task discrimination caused by sparse, target-focused tracking supervision, we propose a decoupled representation learning strategy that offers dense, complementary guidance to improve target-background separation and fusion quality. A nearest-neighbor dynamic template update further enhances robustness to appearance changes. Extensive experiments on four RGBT tracking benchmarks show that TPF achieves competitive accuracy and speed, outperforming both feature-level and existing pixel-level fusion methods, offering new insights into efficient RGBT tracking.