activity
20202025
most citedOPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization

8 citations · 12 across the 5 of their papers we have counts for

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8 papers · 1 filter

cs.CV2025

LLaVA-ReID: Selective Multi-image Questioner for Interactive Person Re-Identification

Yiding Lu, Mouxing Yang, Dezhong Peng +3

Traditional text-based person ReID assumes that person descriptions from witnesses are complete and provided at once. However, in real-world scenarios, such descriptions are often…

cs.CV2023

Cross-modal Active Complementary Learning with Self-refining Correspondence

Yang Qin, Yuan Sun, Dezhong Peng +3

Recently, image-text matching has attracted more and more attention from academia and industry, which is fundamental to understanding the latent correspondence across visual and te…

cs.CV2023

Decoupled Contrastive Multi-View Clustering with High-Order Random Walks

Yiding Lu, Yijie Lin, Mouxing Yang +3

In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i.e.…

cs.CV2023

Noisy-Correspondence Learning for Text-to-Image Person Re-identification

Yang Qin, Yingke Chen, Dezhong Peng +3

Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query. Although nume…

cs.CV2023

Semantic Invariant Multi-view Clustering with Fully Incomplete Information

Pengxin Zeng, Mouxing Yang, Yiding Lu +3

Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly af…

cs.CV20233 cited

Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval

Xu Wang, Dezhong Peng, Ming Yan +1

Cross-domain image retrieval aims at retrieving images across different domains to excavate cross-domain classificatory or correspondence relationships. This paper studies a less-t…