Adaptable Cross-Modality Interactive RGB-T Tracking for Extreme Scenarios

RGB-thermal (RGB-T) tracking leverages the complementary advantages of visible light and thermal infrared sensors to enhance robustness under varying illumination, occlusion, and background clutter. However, existing fusion methods still suffer from redundant feature representations and insufficient cross-modal interaction, which constrain their adaptability in complex environments. To overcome these challenges, we propose an Adaptive Cross-Modality Interaction (ACMI) framework based on the vision transformer (ViT), which enables dynamic and bidirectional information exchange between red–green–blue (RGB) and thermal infrared( TIR) modalities. Within ACMI, Shallow Feature Enhancement (SFE) submodule adaptively reweights modality-specific shallow features in a data-driven manner to strengthen discriminative representations; the DynamicFusion module jointly models spatial–channel dependencies to enhance informative cross-sensor correlations; SEAdapter introduces residual calibration to adaptively balance modality contributions and mitigate information loss. These modules are embedded throughout the Transformer layers, forming a hierarchical architecture that progressively strengthens cross-modal feature understanding. Extensive experiments demonstrate that ACMI achieves consistent performance improvements across multiple RGB-T tracking benchmarks, particularly in low-resolution and cluttered scenes, while maintaining real-time performance. The results validate ACMI as a robust and adaptive framework for efficient multi-sensor visual tracking.