ITSELF: Attention Guided Fine-Grained Alignment for Vision-Language Retrieval

• URL: http://arxiv.org/abs/2601.01024v1
• Date: Sat, 03 Jan 2026 01:19:36 GMT
• Title: ITSELF: Attention Guided Fine-Grained Alignment for Vision-Language Retrieval
• Abstract summary: We introduce ITSELF, an attention-guided framework for implicit local alignment.<n>At its core, Guided Representation with Attentive Bank (GRAB) converts the model's own attention into an Attentive Bank of high-saliency tokens.<n>We also introduce Multi-Layer Attention for Robust Selection (MARS), which aggregates attention across layers and performs diversity-aware top-k selection.
• Score: 7.809330758164896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision Language Models (VLMs) have rapidly advanced and show strong promise for text-based person search (TBPS), a task that requires capturing fine-grained relationships between images and text to distinguish individuals. Previous methods address these challenges through local alignment, yet they are often prone to shortcut learning and spurious correlations, yielding misalignment. Moreover, injecting prior knowledge can distort intra-modality structure. Motivated by our finding that encoder attention surfaces spatially precise evidence from the earliest training epochs, and to alleviate these issues, we introduceITSELF, an attention-guided framework for implicit local alignment. At its core, Guided Representation with Attentive Bank (GRAB) converts the model's own attention into an Attentive Bank of high-saliency tokens and applies local objectives on this bank, learning fine-grained correspondences without extra supervision. To make the selection reliable and non-redundant, we introduce Multi-Layer Attention for Robust Selection (MARS), which aggregates attention across layers and performs diversity-aware top-k selection; and Adaptive Token Scheduler (ATS), which schedules the retention budget from coarse to fine over training, preserving context early while progressively focusing on discriminative details. Extensive experiments on three widely used TBPS benchmarks showstate-of-the-art performance and strong cross-dataset generalization, confirming the effectiveness and robustness of our approach without additional prior supervision. Our project is publicly available at https://trhuuloc.github.io/itself

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