5 accepted papers
Composed Image Retrieval (CIR) aims to retrieve a target image using a query that combines a reference image and a textual description, benefiting users to express their intent more effectively. Despite significant advances in CIR methods, two unresolved problems remain: 1) existing methods overlook…
Federated learning (FL) provides a decentralized machine learning paradigm where a server collaborates with a group of clients to learn a global model without accessing the clients' data. User heterogeneity is a significant challenge for FL, which together with the class-distribution imbalance furth…
Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match specified persons in infrared images to visible images without annotations, and vice versa. USVI-ReID is a challenging yet underexplored task. Most existing methods address the USVI-ReID through cluster-based contrastiv…
"Unsupervised visible-infrared person re-identification (USL-VI-ReID) is a promising yet highly challenging retrieval task. The key challenges in USL-VI-ReID are to accurately generate pseudo-labels and establish pseudo-label correspondences across modalities without relying on any prior annotations…
Visible-infrared person re-identification (VI-ReID) aims to match a specific person from a gallery of images captured from non-overlapping visible and infrared cameras. Most works focus on fully supervised VI-ReID, which requires substantial cross-modality annotation that is more expensive than the…