Enhanced Semantic Alignment in Transformer Tracking via Position Learning and Force-Directed Attention.

In the field of visual object tracking, one-stream pipelines have become the mainstream framework due to their efficient integration of feature extraction and relationship modeling. However, existing methods still face the issue of semantic misalignment: firstly, during feature extraction, the interaction of position encoding between the template and the search image branches leads to a misalignment between feature semantics and position encoding; secondly, traditional attention mechanisms indiscriminately sum the weights of various features, overlooking the complex interactions between them. To address these issues, we propose an Enhanced Semantic Alignment Transformer Tracker (ESAT) from both positional encoding and attention levels. At position embedding level, by leveraging Dual-Label Supervised Position Encoding Semantic Alignment Module (DLSPE-SAM), ESAT separately learns the position information of the target and search branches and distinguishes whether the position belongs to the target area or the background, thereby enhancing the semantic consistency between position and features. At attention level, ESAT incorporates a repulsion-attraction mechanism applied to the self-attention module, named Force-Directed Attention Semantic Alignment Module (FDA-SAM), adjusting attention scores and enhancing semantic relationships among features. Extensive experiments on multiple public tracking datasets show that our method outperforms many pipelines and achieves superior performance on five challenging benchmarks.