Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking

Single object tracking aims to locate one specific target in video sequences, given its initial state. Classical trackers rely solely on visual cues, restricting their ability to handle challenges such as appearance variations, ambiguity, and distractions. Hence, Vision-Language Tracking (VLT) has emerged as a promising approach, incorporating language descriptions to directly provide high-level semantics and enhance tracking performance. However, current Vision-Language (VL) trackers have not fully exploited the power of multi-modal learning, as they suffer from limitations such as heavily relying on off-the-shelf backbones for feature extraction, ineffective asynchronous fusion designs, and the absence of VL-related loss functions for optimizing multi-modal representation. Consequently, we present a novel tracker that progressively explores target-centric semantics for VLT. Specifically, we propose the first Synchronous Learning Backbone (SLB) for VLT, which consists of two novel modules: the Target Enhance Module (TEM) and the Semantic-Aware Module (SAM). These modules together ensure the multi-modal feature extraction and interaction at the same pace, facilitating the tracker to synchronously perceive target-related semantics from both visual and textual modalities. Moreover, we devise the dense matching loss to further strengthen multi-modal representation learning. Extensive experiments on VLT datasets demonstrate the superiority and effectiveness of our methods.