TL;DR
The proposed Cross-Modal Adaptive Token Selection and Alignment Network (CATSANet), a CLIP-based framework tailored for TI-ReID, achieves competitive performance in terms of Rank-k accuracy and mAP, demonstrating the effectiveness of fine-grained alignment and ranking refinement across datasets.
A new ITM framework that improves the model's discriminative performance by focusing on localized core attributes and has a better robustness in handling highly similar hard negatives, which provides a new way to address cross-modal hard sample discrimination.
This approach introduces a Paired-Guided Consistency Rectification (PCR) module that utilizes strictly paired data as reliable anchors to dynamically calibrate the confidence of unpaired samples, effectively suppressing noise from inconsistent associations under mild-to-moderate corruptions.
Text-based person anomaly search requires retrieving real-world pedestrian images from detailed natural-language descriptions using models trained primarily on synthetic data. This Sim2Real setting is particularly challenging because visually similar candidates may differ only in subtle actions, object interactions, or...
This work proposes CLIP-SGI, a semantic-guided and instance-consistent framework for generalizable person ReID that combines semantic guidance, domain-aware representation learning, and instance consistency to improve robustness under domain shifts.
This study designs a Text-Aware Patch Selection module, which dynamically constructs patch-level regions for fine-grained excavation based on informative token selection, effectively bypassing the need for bounding boxes, and introduces a Cross-Grained Alignment module to learn modality-shared features across global an...
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