The Chinese University of Hong Kong（香港中文大学）

AEGIS: Exploring the Limit of World Knowledge Capabilities for Unified Mulitmodal Models

AEGIS：探索统一多模态模型世界知识能力的极限

Jintao Lin, Bowen Dong, Weikang Shi, Chenyang Lei, Suiyun Zhang, Rui Liu, Xihui Liu

机构 * University of Hong Kong（香港大学） ； The Hong Kong Polytechnic University（香港理工大学） ； The Chinese University of Hong Kong（香港中文大学） ； Huawei Research（华为研究） 纠错

AI总结 AEGIS通过多任务基准测试和确定性检查表评估，揭示统一多模态模型在世界知识推理中的不足，并指出简化推理模块可缓解其缺陷。

Test-time generative augmentation for medical image segmentation

测试时生成增强用于医学图像分割

Xiao Ma, Yuhui Tao, Zetian Zhang, Yuhan Zhang, Xi Wang, Sheng Zhang, Zexuan Ji, Yizhe Zhang, Qiang Chen, Guang Yang

机构 * organization= School of Computer Science ； Engineering, Nanjing University of Science ； organization= Bioengineering Department ； Imperial-X, Imperial College London , city= London , postcode= W12 7SL , country= UK ； organization= Digital Medical Research Center, School of Basic Medical Sciences, Fudan University , city= Shanghai , country= China ； organization= Shanghai Key Laboratory of MICCAI , city= Shanghai , country= China ； organization= School of Biomedical Engineering, Shenzhen University , city= Shenzhen , country= China ； organization= Department of Computer Science ； Engineering, The Hong Kong University of Science ； Engineering, The Chinese University of Hong Kong , city= Hong Kong , country= China ； Lung Institute, Imperial College London , city= London , postcode= SW7 2AZ , country= UK ； organization= Cardiovascular Research Centre, Royal Brompton Hospital , city= London , postcode= SW3 6NP , country= UK ； organization= School of Biomedical Engineering \& Imaging Sciences, King's College London , city= London , postcode= WC2R 2LS , country= UK 纠错

AI总结 本研究提出TTGA方法，通过生成模型在测试时增强医学图像分割，提升分割精度并提供像素级误差估计。

Comments Accepted for publication in Medical Image Analysis (MedIA). Finalized version. Vol. 109, March 2026

Journal ref Medical Image Analysis, Vol. 109, 103902, 2026