activity
20242026
most citedUnity in Diversity: Multi-expert Knowledge Confrontation and Collaboration for Generalizable Vehicle Re-identification

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

LG-PF: Lightweight Confidence-Guided Polarization Image Fusion

Zhuangfan Huang, Zhenyu Kuang, Gao Wang +3

Polarization image fusion combines the stable luminance and structural information of the total- intensity image S0 with the material-sensitive details of the degree of linear pola…

cs.CV2026

GeoLink: A 3D-aware Framework to Improve Generalization for Cross-view Geo-localization

Hongyang Zhang, Yinhao Liu, Haitao Zhang +4

Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. Its core difficulty lies in severe…

cs.CV2025

SkyLink: Unifying Street-Satellite Geo-Localization via UAV-Mediated 3D Scene Alignment

Hongyang Zhang, Yinhao Liu, Zhenyu Kuang

Cross-view geo-localization aims at establishing location correspondences between different viewpoints. Existing approaches typically learn cross-view correlations through direct f…

cs.CV2025

AWM-Fuse: Multi-Modality Image Fusion for Adverse Weather via Global and Local Text Perception

Xilai Li, Huichun Liu, Xiaosong Li +3

Multi-modality image fusion (MMIF) in adverse weather aims to address the loss of visual information caused by weather-related degradations, providing clearer scene representations…

cs.CV2025

MambaTrans: Multimodal Fusion Image Translation via Large Language Model Priors for Downstream Visual Tasks

Yushen Xu, Xiaosong Li, Zhenyu Kuang +3

The goal of multimodal image fusion is to integrate complementary information from infrared and visible images, generating multimodal fused images for downstream tasks. Existing do…

cs.CV2024★ 1 cited

Unity in Diversity: Multi-expert Knowledge Confrontation and Collaboration for Generalizable Vehicle Re-identification

Zhenyu Kuang, Hongyang Zhang, Mang Ye +5

Generalizable vehicle re-identification (ReID) seeks to develop models that can adapt to unknown target domains without the need for additional fine-tuning or retraining. Previous…