Scientific expertise often requires recognizing subtle visual differences that remain challenging to articulate even for domain experts. We present a system that leverages generative models to automatically discover and visualize minimal discriminative features between categories while preserving instance identity. Our method generates counterfactual visualizations with subtle, targeted transformations between classes, performing well even in domains where data is sparse, examples are unpaired, and category boundaries resist verbal description. Experiments across six domains, including black hole simulations, butterfly taxonomy, and medical imaging, demonstrate accurate transitions with limited training data, highlighting both established discriminative features and novel subtle distinctions that measurably improved category differentiation. User studies confirm our generated counterfactuals significantly outperform traditional approaches in teaching humans to correctly differentiate between fine-grained classes, showing the potential of generative models to advance visual learning and scientific research.
MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction
Faculty of Applied Sciences, Macao Polytechnic University（应用科学学院，澳门理工学院）；Department of Electrical Engineering, Zhejiang University（电气工程学院，浙江大学）；Department of Computer Science, The University of Adelaide（计算机科学学院，阿德莱德大学）；Department of Computer Science, University of Birmingham（计算机科学学院，伯明翰大学）
Integrating Anatomical Priors into a Causal Diffusion Model
Department of Electrical Engineering, Stanford University, Stanford, CA, USA（电气工程系，斯坦福大学）；Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学）
Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena
University of South Carolina（南卡罗来纳大学）；AI Research Lab, HPE Labs, Hewlett Packard Enterprise（HPE实验室人工智能研究部）；Indian AI Research Organization（印度人工智能研究组织）；General Atomics（通用原子公司）
Adaptive Routing for Efficient Diffusion Transformer-Based PNI Prediction
Seoul National University（首尔国立大学）；Kyung Hee University（庆熙大学）；OUTTA；Chung-Ang University（Chung-Ang 大学）；Seoul National University School of Medicine（首尔国立大学医学院）；Samsung Changwon Hospital（三星昌原医院）；Samsung Medical Center（三星医疗中心）；NVIDIA AI Technology Center（NVIDIA AI 技术中心）
Implicit Bias Injection Attacks against Text-to-Image Diffusion Models
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Mathematics and Statistics, Wuhan University（武汉大学数学与统计学院）；School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络空间安全学院）
