Augmentative fusion network for robust RGBT tracking

RGBT tracking aims to take full advantage of the complementary advantages of RGB and Thermal Infrared (TIR) modalities to achieve robust tracking in complex scenes. However, current approaches face limitations when dealing with the quality-imbalanced problem. In this paper, we introduce a novel augmentative fusion learning framework that aims to maximize the potential of existing fusion modules in modality quality imbalanced scenarios. In particular, we design a modality stochastic degradation strategy to improve the robustness of the fusion module in modality quality imbalance scenarios. Meanwhile, to further enhance the fusion performance with the modality quality imbalance inputs, a self-supervised constraint is introduced to reconstruct the modality features before degradation by combining high- quality modality and degraded modality information. Finally, the effectiveness of the proposed method is verified by evaluating it on two standard RGBT datasets and two state-of-the-art algorithms. And the results indicate that our method achieved superior performance without adding any parameters or computational complexity.