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20182026
most citedEntropy Minimization vs. Diversity Maximization for Domain Adaptation

13 citations · 35 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

DCP-CLIP:A Coarse-to-Fine Framework for Open-Vocabulary Semantic Segmentation with Dual Interaction

Jing Wang, Huimin Shi, Quan Zhou +3

The recent years have witnessed the remarkable development for open-vocabulary semantic segmentation (OVSS) using visual-language foundation models, yet still suffer from following…

cs.CV2025

Dynamic Contrastive Learning for Hierarchical Retrieval: A Case Study of Distance-Aware Cross-View Geo-Localization

Suofei Zhang, Xinxin Wang, Xiaofu Wu +2

Existing deep learning-based cross-view geo-localization methods primarily focus on improving the accuracy of cross-domain image matching, rather than enabling models to comprehens…

cs.CV20219 cited

FPB: Feature Pyramid Branch for Person Re-Identification

Suofei Zhang, Zirui Yin, Xiofu Wu +3

High performance person Re-Identification (Re-ID) requires the model to focus on both global silhouette and local details of pedestrian. To extract such more representative feature…

cs.CV20203 cited

Branch-Cooperative OSNet for Person Re-Identification

Lei Zhang, Xiaofu Wu, Suofei Zhang +1

Multi-branch is extensively studied for learning rich feature representation for person re-identification (Re-ID). In this paper, we propose a branch-cooperative architecture over…

cs.CV20204 cited

Diversity-Achieving Slow-DropBlock Network for Person Re-Identification

Xiaofu Wu, Ben Xie, Shiliang Zhao +3

A big challenge of person re-identification (Re-ID) using a multi-branch network architecture is to learn diverse features from the ID-labeled dataset. The 2-branch Batch DropBlock…

cs.CV20206 cited

Learning Diverse Features with Part-Level Resolution for Person Re-Identification

Ben Xie, Xiaofu Wu, Suofei Zhang +2

Learning diverse features is key to the success of person re-identification. Various part-based methods have been extensively proposed for learning local representations, which, ho…