Progressively Spiral Mamba Fusion for Multimodal Tracking

Multimodal object tracking has received extensive attentiondue to its strong robustness and complementary collaboration. However,most existing methods only evaluate the cross-modal fusion at specificmulti-modal layers, and overlook the interaction among different fea-ture layers from single modal, which limits the utilization and propa-gation of cross-modal complementary information and consequently de-grades tracking performance. To alleviate this issue, we propose a pro-gressive multimodal tracker based on State Space Model (SSM) namedPSMTrack, which consists of three key modules. First, Difference-guidedBidirectional State-space Mamba Enhancement (DBME) is developedto leverage difference-aware cues and bidirectional SSM modeling toprogressively enhance modality representations for subsequent tracking,while suppressing background and similar distractions. Second, we adoptan expert routing mechanism to adaptive select a subset of layers andperform cross-layer interaction to alleviate the information loss causedby relying solely on the last layer. Finally, we design a Hybrid SpiralMamba Fusion (HSMF) module that performs dynamic spatial modelingwith spiral Mamba from both local and global scales for final cross-modalfusion, thereby capturing long-range dependencies with near-linear com-plexity and improving bounding-box regression stability. Extensive ex-periments on multiple mainstream RGB-X benchmarks validate that ourmodel consistently improves both accuracy and robustness.