iamese and Transformer trackers have demon strated exceptional performance in visual object tracking. These methods utilize initial and potentially online templates to locate the target in subsequent frames. Despite their success, these trackers are vulnerable to changes in the target's appearance due to slow template updates and interference from similar objects, resulting from the absence of scene information. To address these issues, we introduce a reference region within our tracker. The reference region is updated rapidly, providing short-term scene information. By associating the initial template, reference region, and current search region, we enhance the tracker's ability to adapt to changes in target appearance and discriminate between the target and other objects. Additionally, we propose a novel Reference-Enhance (RE) module, which aggregates contextually relevant information from the reference region to enhance the template feature. Extensive experiments show our method achieves state-of-the-art performance on six popular visual object tracking benchmarks while running at over 40 FPS.

RGB-T tracking aims to leverage the mutual enhancement and complement ability of RGB and TIR modalities for improving the tracking process in various scenarios, where cross-modal interaction is the key component. Some previous methods concatenate the RGB and TIR search region features directly to perform a coarse interaction process with redundant background noises introduced. Many other methods sample candidate boxes from search frames and conduct various fusion approaches on isolated pairs of RGB and TIR boxes, which limits the cross-modal interaction within local regions and brings about inadequate context modeling. To alleviate these limitations, we propose a novel Template-Bridged Search region Interaction (TBSI) module which exploits templates as the medium to bridge the cross-modal interaction between RGB and TIR search regions by gathering and distributing target-relevant object and environment contexts. Original templates are also updated with enriched multimodal contexts from the template medium. Our TBSI module is inserted into a ViT backbone for joint feature extraction, search-template matching, and cross-modal interaction. Extensive experiments on three popular RGB-T tracking benchmarks demonstrate our method achieves new state-of-the-art performances. Code is available at https://github.com/RyanHTR/TBSI.