Enhanced Semantic Alignment in Transformer Tracking via Position Learning and Force-Directed Attention
Keywords: Transformer Tracking; Single Object Tracking; Semantic Alignment; Self-supervised Position Loss; Force-Directed Attention
Abstract: In the field of visual object tracking, one-stream pipelines have become the mainstream framework due to its efficient integration of feature extraction and relationship modeling.
However, existing methods still face the issue of semantic misalignment:
firstly, the interaction of positional encoding between the two branches leads to a misalignment between feature semantics and position encoding;
secondly, traditional attention mechanisms fail to distinguish between semantic attraction and repulsion among features, resulting in semantic misalignment when the model processes these features.
To address these issues, we propose an Enhanced Semantic Alignment Transformer Tracker (ESAT) with position encode learning and force-directed attention mechanism.
By leveraging positional encoding loss, ESAT separately learns the absolute positional encodings of the target and search branches, distinguishing the locations of various tokens and their positive or negative relationships, thereby enhancing the semantic consistency between position and features.
Additionally, it incorporates a repulsion-attraction mechanism applied to the self-attention module, simulating dynamic interactions between nodes to improve feature discrimination.
Extensive experiments on multiple public tracking datasets show that our method outperforms many pipelines and achieves superior performance on five challenging benchmarks.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 1288
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