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

• URL: http://arxiv.org/abs/2311.17085v2
• Date: Mon, 19 Feb 2024 10:32:13 GMT
• Title: Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking
• Authors: Jiawei Ge, Xiangmei Chen, Jiuxin Cao, Xuelin Zhu, Bo Liu
• Abstract summary: Single object tracking aims to locate one specific target in video sequences, given its initial state. Vision-Language (VL) tracking has emerged as a promising approach. We present a novel tracker that progressively explores target-centric semantics for VL tracking.
• Score: 3.416427651955299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 (VL) tracking has emerged as a promising approach, incorporating language descriptions to directly provide high-level semantics and enhance tracking performance. However, current VL trackers have not fully exploited the power of VL learning, as they suffer from limitations such as heavily relying on off-the-shelf backbones for feature extraction, ineffective VL fusion designs, and the absence of VL-related loss functions. Consequently, we present a novel tracker that progressively explores target-centric semantics for VL tracking. Specifically, we propose the first Synchronous Learning Backbone (SLB) for VL tracking, which consists of two novel modules: the Target Enhance Module (TEM) and the Semantic Aware Module (SAM). These modules enable the tracker to perceive target-related semantics and comprehend the context of both visual and textual modalities at the same pace, facilitating VL feature extraction and fusion at different semantic levels. Moreover, we devise the dense matching loss to further strengthen multi-modal representation learning. Extensive experiments on VL tracking datasets demonstrate the superiority and effectiveness of our methods.

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