National University of Singapore（新加坡国立大学）

MedGEN-Bench: Contextually entangled benchmark for open-ended multimodal medical generation

Junjie Yang, Yuhao Yan, Gang Wu, Yuxuan Wang, Ruoyu Liang, Xinjie Jiang, Xiang Wan, Fenglei Fan, Yongquan Zhang, Feiwei Qin, Changmiao Wang

机构 * South China University of Technology（华南理工大学） ； Sun Yat-sen University（中山大学） ； Hangzhou Dianzi University（杭州电子科技大学） ； Zhejiang University of Finance & Economics（浙江财经大学） ； National University of Singapore（新加坡国立大学） ； Shenzhen Research Institute of Big Data（深圳大数据研究院） ； City University of Hong Kong（香港城市大学） 纠错

Comments CVPR 2026 Under Review

Flood-LDM: Generalizable Latent Diffusion Models for rapid and accurate zero-shot High-Resolution Flood Mapping

Sun Han Neo, Sachith Seneviratne, Herath Mudiyanselage Viraj Vidura Herath, Abhishek Saha, Sanka Rasnayaka, Lucy Amanda Marshall

机构 * Department of Computer Science, School of Computing, National University of Singapore（新加坡国立大学计算机科学系） ； Transport, Health and Urban Systems Research Lab, Melbourne School of Design, University of Melbourne（墨尔本大学设计学院交通、健康与城市系统研究实验室） ； School of Civil Engineering, Faculty of Engineering, University of Sydney（悉尼大学土木工程学院） ； Delft Institute of Applied Mathematics, Delft University of Technology（代尔夫特理工大学应用数学研究所） 纠错

Comments Accepted for publication at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026

Blurred Encoding for Trajectory Representation Learning

Silin Zhou, Yao Chen, Shuo Shang, Lisi Chen, Bingsheng He, Ryosuke Shibasaki

机构 * University of Electronic Science and Technology of China（电子科技大学） ； National University of Singapore（新加坡国立大学） ； LocationMind Inc.（LocationMind公司） 纠错

Comments This paper is accepted by KDD2025(Feb. Cycle)

How does My Model Fail? Automatic Identification and Interpretation of Physical Plausibility Failure Modes with Matryoshka Transcoders

Yiming Tang, Abhijeet Sinha, Dianbo Liu

机构 * National University of Singapore（新加坡国立大学） 纠错

DRIP: Defending Prompt Injection via Token-wise Representation Editing and Residual Instruction Fusion

Ruofan Liu, Yun Lin, Zhiyong Huang, Jin Song Dong

机构 * National University of Singapore（新加坡国立大学） ； Shanghai Jiao Tong University（上海交通大学） 纠错

UniVST: A Unified Framework for Training-free Localized Video Style Transfer

Quanjian Song, Mingbao Lin, Wengyi Zhan, Shuicheng Yan, Liujuan Cao, Rongrong Ji

机构 * Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学，中国） ； Rakuten Asia Pte. Ltd. ； School of Computing, National University of Singapore（新加坡国立大学计算机学院） ； Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院） 纠错

Comments Accepted by TPAMI 2025; Project Page: https://quanjiansong.github.io/projects/UniVST