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20222026
most citedNeighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification

4 citations · 4 across the 2 of their papers we have counts for

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8 papers · 1 filter

cs.CV2026

Dual-Branch Cross-Projection Debiasing through Diffusion-based Disentanglement

Xiangqian Zhao, Xinyang Jiang, Zhipeng Xu +5

Foundation models trained on biased datasets often rely on spurious correlations between target labels and non-causal attributes, resulting in poor generalization on minority group…

cs.CV2025

Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification

Haonan Shi, Yubin Wang, De Cheng +3

Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets by reducing the modali…

cs.CV2025

Harnessing Textual Semantic Priors for Knowledge Transfer and Refinement in CLIP-Driven Continual Learning

Lingfeng He, De Cheng, Di Xu +2

Continual learning (CL) aims to equip models with the ability to learn from a stream of tasks without forgetting previous knowledge. With the progress of vision-language models lik…

cs.CV2025

EKPC: Elastic Knowledge Preservation and Compensation for Class-Incremental Learning

Huaijie Wang, De Cheng, Lingfeng He +4

Class-Incremental Learning (CIL) aims to enable AI models to continuously learn from sequentially arriving data of different classes over time while retaining previously acquired k…

cs.CV2025

Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID

De Cheng, Lingfeng He, Nannan Wang +2

Unsupervised visible-infrared person re-identification (USL-VI-ReID) seeks to match pedestrian images of the same individual across different modalities without human annotations f…

cs.CV2025

StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning

Huaijie Wang, De Cheng, Guozhang Li +5

Video Class-Incremental Learning (VCIL) seeks to develop models that continuously learn new action categories over time without forgetting previously acquired knowledge. Unlike tra…