activity
20172026
most citedRobust Real-World Image Super-Resolution against Adversarial Attacks

923 citations · 1.2k across the 30 of their papers we have counts for

collaborators

30 papers

cs.CV2026

MoonSplat: Monocular Online Gaussian Splatting with Sim(3) Global Optimization

Guo Pu, Yixuan Han, Haofeng Li +3

Online 3D reconstruction from monocular image sequences is a challenging and ongoing research topic. 3D Gaussian Splatting (3DGS), leveraging its high-quality real-time rendering c…

cs.AI2026

Collaborative Multi-Agent Scripts Generation for Enhancing Imperfect-Information Reasoning in Murder Mystery Games

Keyang Zhong, Junlin Xie, Hefeng Wu +2

Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multiplayer game settings with imperfect…

cs.AI2024

JieHua Paintings Style Feature Extracting Model using Stable Diffusion with ControlNet

Yujia Gu, Haofeng Li, Xinyu Fang +2

This study proposes a novel approach to extract stylistic features of Jiehua: the utilization of the Fine-tuned Stable Diffusion Model with ControlNet (FSDMC) to refine depiction t…

cs.CV2024★ 1 cited

Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models

Haifan Gong, Yitao Wang, Yihan Wang +3

The scarcity and complexity of voxel-level annotations in 3D medical imaging present significant challenges, particularly due to the domain gap between labeled datasets from well-r…

eess.IV2024

Intensity Confusion Matters: An Intensity-Distance Guided Loss for Bronchus Segmentation

Haifan Gong, Wenhao Huang, Huan Zhang +5

Automatic segmentation of the bronchial tree from CT imaging is important, as it provides structural information for disease diagnosis. Despite the merits of previous automatic bro…

cs.CV2024★ 1 cited

Self-Supervised Alignment Learning for Medical Image Segmentation

Haofeng Li, Yiming Ouyang, Xiang Wan

Recently, self-supervised learning (SSL) methods have been used in pre-training the segmentation models for 2D and 3D medical images. Most of these methods are based on reconstruct…