works on

From the 1 of 29 linked papers with an AI index.

activity
20242026
collaborators

29 papers

cs.SI2026

Comprehensive, Efficient Large-Scale Community Detection via Structural Entropy Game

Pu Li, Yantuan Xian, Hao Peng +4

The paper introduces CoDeSEG, a heuristic algorithm that detects non‑overlapping, overlapping, and dynamic communities in very large graphs by minimizing a two‑dimensional structur…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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),…

cs.CV2026

[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…