38 citations · 95 across the 44 of their papers we have counts for
11 papers · 1 filter
Hierarchical Prompt Learning for Image- and Text-Based Person Re-Identification
Linhan Zhou, Shuang Li, Neng Dong +3
Person re-identification (ReID) aims to retrieve target pedestrian images given either visual queries (image-to-image, I2I) or textual descriptions (text-to-image, T2I). Although b…
DINOv2 Driven Gait Representation Learning for Video-Based Visible-Infrared Person Re-identification
Yujie Yang, Shuang Li, Jun Ye +3
Video-based Visible-Infrared person re-identification (VVI-ReID) aims to retrieve the same pedestrian across visible and infrared modalities from video sequences. Existing methods…
A Luminance-Aware Multi-Scale Network for Polarization Image Fusion with a Multi-Scene Dataset
Zhuangfan Huang, Xiaosong Li, Gao Wang +3
Polarization image fusion combines S0 and DOLP images to reveal surface roughness and material properties through complementary texture features, which has important applications i…
FlexiD-Fuse: Flexible number of inputs multi-modal medical image fusion based on diffusion model
Yushen Xu, Xiaosong Li, Yuchun Wang +3
Different modalities of medical images provide unique physiological and anatomical information for diseases. Multi-modal medical image fusion integrates useful information from dif…
FS-Diff: Semantic guidance and clarity-aware simultaneous multimodal image fusion and super-resolution
Yuchan Jie, Yushen Xu, Xiaosong Li +3
As an influential information fusion and low-level vision technique, image fusion integrates complementary information from source images to yield an informative fused image. A few…
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…
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…
Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning
Yafei Zhang, Lingqi Kong, Huafeng Li +1
To reduce the reliance of visible-infrared person re-identification (ReID) models on labeled cross-modal samples, this paper explores a weakly supervised cross-modal person ReID me…
Dual-Granularity Cross-Modal Identity Association for Weakly-Supervised Text-to-Person Image Matching
Yafei Zhang, Yongle Shang, Huafeng Li
Weakly supervised text-to-person image matching, as a crucial approach to reducing models' reliance on large-scale manually labeled samples, holds significant research value. Howev…
UniFuse: A Unified All-in-One Framework for Multi-Modal Medical Image Fusion Under Diverse Degradations and Misalignments
Dayong Su, Yafei Zhang, Huafeng Li +2
Current multimodal medical image fusion typically assumes that source images are of high quality and perfectly aligned at the pixel level. Its effectiveness heavily relies on these…
SOTA: Self-adaptive Optimal Transport for Zero-Shot Classification with Multiple Foundation Models
Zhanxuan Hu, Qiyu Xu, Yu Duan +2
Foundation models have attracted widespread attention across domains due to their powerful zero-shot classification capabilities. This work is motivated by two key observations: (1…