38 citations · 95 across the 44 of their papers we have counts for
46 papers
Comprehensive, Efficient Large-Scale Community Detection via Structural Entropy Game
Pu Li, Yantuan Xian, Hao Peng +4
Community detection is a critical task in graph theory, social network analysis, and bioinformatics, where communities are defined as clusters of densely interconnected nodes. Howe…
Multi-modality Image Fusion under Adverse Weather: Mask-Guided Feature Restoration and Interaction
Xilai Li, Xiaosong Li, Haishu Tan +3
Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degr…
One Stone, Three Birds: Self-adaptive Optimal Transport for Multi-VLM Selection, Adaptation, and Ensembling
Qiyu Xu, Zhanxuan Hu, Yu Duan +4
Vision-language models (VLMs) enable visual recognition from semantic class descriptions, which makes them attractive when target annotations are scarce or unavailable. Most deploy…
Geometry-Preserving Unsupervised Alignment for Heterogeneous Foundation Models
Shuwen Yu, Zhanxuan Hu, Yi Zhao +2
Foundation models have driven rapid progress in computer vision, yet the two dominant paradigms, vision-language foundation models (VLMs) and vision-only foundation models (VFMs),…
[CLS] is Not Enough: Multi-Label Recognition via Patch-Level Inference and Adaptive Aggregation
Akang Wang, Xili Deng, Zhanxuan Hu +3
Vision-Language Models such as CLIP exhibit strong zero-shot recognition capability by aligning images with textual concepts, yet they often underperform on multi-label recognition…
Expandable, Compressible, Mineable: Open-World Thermal Image Restoration
Pu Li, Huafeng Li, Yafei Zhang +3
In open-world settings, thermal infrared (TIR) image degradations continuously emerge and evolve, while most existing all-in-one restoration methods are built on a closed-set assum…
Breaking Degradation Coupling: A Structural Entropy Guided Decoupled Framework and Benchmark for Infrared Enhancement
Pu Li, Huafeng Li, Yafei Zhang +2
Thermal infrared image enhancement aims to restore high-quality images from complex compound degradations. Existing all-in-one approaches typically employ a single shared backbone…
Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion
Zengyi Yang, Yu Liu, Juan Cheng +3
Infrared-visible image fusion aims to integrate complementary information for robust visual understanding, but existing fusion methods struggle with simultaneously adapting to mult…
Degradation-Robust Fusion: An Efficient Degradation-Aware Diffusion Framework for Multimodal Image Fusion in Arbitrary Degradation Scenarios
Yu Shi, Yu Liu, Zhong-Cheng Wu +3
Complex degradations like noise, blur, and low resolution are typical challenges in real world image fusion tasks, limiting the performance and practicality of existing methods. En…
MAVFusion: Efficient Infrared and Visible Video Fusion via Motion-Aware Sparse Interaction
Xilai Li, Weijun Jiang, Xiaosong Li +5
Infrared and visible video fusion combines the object saliency from infrared images with the texture details from visible images to produce semantically rich fusion results. Howeve…
Missing No More: Dictionary-Guided Cross-Modal Image Fusion under Missing Infrared
Yafei Zhang, Meng Ma, Huafeng Li +1
Infrared-visible (IR-VIS) image fusion is vital for perception and security, yet most methods rely on the availability of both modalities during training and inference. When the in…
Adaptive Dynamic Dehazing via Instruction-Driven and Task-Feedback Closed-Loop Optimization for Diverse Downstream Task Adaptation
Yafei Zhang, Shuaitian Song, Huafeng Li +2
In real-world vision systems,haze removal is required not only to enhance image visibility but also to meet the specific needs of diverse downstream tasks.To address this challenge…
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…
BSAFusion: A Bidirectional Stepwise Feature Alignment Network for Unaligned Medical Image Fusion
Huafeng Li, Dayong Su, Qing Cai +1
If unaligned multimodal medical images can be simultaneously aligned and fused using a single-stage approach within a unified processing framework, it will not only achieve mutual…