TL;DR
Experimental results on multiple benchmark datasets demonstrate that the UGCL framework exhibits favorable robustness and competitive performance under both noisy and noise-free correspondence conditions.
Dynamic Uncertainty with Noisy Correspondences (DUNC), a novel framework that incorporates two key components that incorporates Cross-modal Evidential Learning (CEL), which models bidirectional alignment uncertainty using a Dirichlet distribution to capture the confidence in image–text similarity, and Dynamic Robust Lo...
Unsupervised person re-identification (USL-ReID) typically relies on clustering to generate pseudo-labels, but significant cross-view appearance variations often cause images of the same identity to be split into different clusters. Training on such noisy pseudo-labels severely degrades the learned representations. The...
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.
Structural-Semantic Reciprocal Learning (SSRL), a framework that transforms open-loop association into a self-correcting closed-loop system, achieves robust cross-modal representation through the reciprocal interaction between structural and semantic learning.
A unified Partition-based Information Rebalancing (PaIR) framework to realize balanced optimization and precise alignment of cross-modal information from both global content and local part dimensions and to eliminate global–local semantic discontinuity and enhance fine-grained cross-modal matching capability.
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