  Learning robust multi-modal feature representations is critical for boosting
tracking performance. To this end, we propose a novel X Modality Assisting
Network (X-Net) to shed light on the impact of the fusion paradigm by
decoupling the visual object tracking into three distinct levels, facilitating
subsequent processing. Firstly, to tackle the feature learning hurdles stemming
from significant differences between RGB and thermal modalities, a
plug-and-play pixel-level generation module (PGM) is proposed based on
self-knowledge distillation learning, which effectively generates X modality to
bridge the gap between the dual patterns while reducing noise interference.
Subsequently, to further achieve the optimal sample feature representation and
facilitate cross-modal interactions, we propose a feature-level interaction
module (FIM) that incorporates a mixed feature interaction transformer and a
spatial-dimensional feature translation strategy. Ultimately, aiming at random
drifting due to missing instance features, we propose a flexible online
optimized strategy called the decision-level refinement module (DRM), which
contains optical flow and refinement mechanisms. Experiments are conducted on
three benchmarks to verify that the proposed X-Net outperforms state-of-the-art
trackers.
