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Learning robust multi-modal feature representations is critical for boostingtracking performance. To this end, we propose a novel X Modality AssistingNetwork (X-Net) to shed light on the impact of the fusion paradigm bydecoupling the visual object tracking into three distinct levels, facilitatingsubsequent processing. Firstly, to tackle the feature learning hurdles stemmingfrom significant differences between RGB and thermal modalities, aplug-and-play pixel-level generation module (PGM) is proposed based onself-knowledge distillation learning, which effectively generates X modality tobridge the gap between the dual patterns while reducing noise interference.Subsequently, to further achieve the optimal sample feature representation andfacilitate cross-modal interactions, we propose a feature-level interactionmodule (FIM) that incorporates a mixed feature interaction transformer and aspatial-dimensional feature translation strategy. Ultimately, aiming at randomdrifting due to missing instance features, we propose a flexible onlineoptimized strategy called the decision-level refinement module (DRM), whichcontains optical flow and refinement mechanisms. Experiments are conducted onthree benchmarks to verify that the proposed X-Net outperforms state-of-the-arttrackers.

X Modality Assisting RGBT Object Tracking