A Stable Self-evolving Target Template Helps Test-Time Adaptation in Multimodal Object Tracking

RGB-X multimodal trackers have recently achieved remarkable progress by exploiting complementary information from heterogeneous modalities. However, existing methods often overlook performance degradation caused by distribution shifts between training and testing environments. Although recent test-time adaptation approaches have been explored for multimodal tracking, they largely rely on unreliable test-time confidence estimates or blind source-domain alignment, resulting in inaccurate adaptation objectives under shifted environments. To address these challenges, we propose S${}^2$TTA, a stable self-evolving target template-guided test-time adaptation framework. S${}^2$TTA constructs a target-specific template prior and performs stability-aware prior evolution to provide reliable and adaptive test-time optimization objectives. Through a decoupled hierarchical adaptation space with shift-aware space selection, S${}^2$TTA further enables selective updates across different modalities while preserving valuable pre-trained knowledge. Extensive experiments demonstrate that S${}^2$TTA consistently achieves superior performance and significantly improves model robustness and generalization ability.