Synthesizing realistic and diverse anomalous samples from limited data is vital for robust model generalization. However, existing methods struggle to reconcile fidelity and diversity, often hampered by distribution misalignment and overfitting, respectively.To mitigate this, we introduce Anomaly Preference Optimization,a novel paradigm that reformulates anomaly generation as a preference learning problem.Central to our approach is an implicit preference alignment mechanism that leverages real anomalies as positive references, deriving optimization signals directly from denoising trajectory deviations without requiring costly human annotation. Furthermore, we propose a Time-Aware Capacity Allocation module that dynamically distributes model capacity along the diffusion timeline,prioritizing structural diversity during highnoise phases while enhancing fine-grained fidelity in low-noise stages. During inference, a hierarchical sampling strategy modulates the coherencealignment trade-off, enabling precise control over generation. Extensive experiments demonstrate that significantly outperforms existing baselines,achieving state-of-the-art performance in both realism and diversity.
Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing
University of Liverpool（利物浦大学）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Edinburgh（爱丁堡大学）；University of Bristol（布里斯托大学）；University of Exeter（埃克塞特大学）
Instant Preference Alignment for Text-to-Image Diffusion Models
New Laboratory of Pattern Recognition, CASIA（模式识别新实验室，中国科学院自动化研究所）；The Hong Kong University of Science and Technology（香港科技大学）；Nanjing university（南京大学）；Academy of Broadcasting Science, NRTA（广播科学研究院，国家广播电视总局）
Double Helix Diffusion for Cross-Domain Anomaly Image Generation
School of Computer Science, Wuhan University（武汉大学计算机学院）；Shenzhen Intellifusion Technologies Co Ltd（深圳智融科技有限公司）；Guangdong Artificial Intelligence and Digital Economy Laboratory (SZ)（广东省人工智能与数字经济实验室）
Compositional Text-to-Image Generation Via Region-aware Bimodal Direct Preference Optimization
Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University（上海智能信息处理关键实验室，复旦大学计算机学院）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）
FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction
Neurosciences Graduate Program, University of California San Diego（加州大学圣地亚哥分校神经科学研究生项目）；Department of Computer Science and Engineering, University of California San Diego（加州大学圣地亚哥分校计算机科学与工程系）；Department of Cognitive Science, University of California San Diego（加州大学圣地亚哥分校认知科学系）；Halıcıoğlu Data Science Institute, University of California San Diego（加州大学圣地亚哥分校Halıcıoğlu数据科学研究所）
MultiCrafter: High-Fidelity Multi-Subject Generation via Disentangled Attention and Identity-Aware Preference Alignment
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件技术学院）；Huawei Technologies Ltd（华为技术有限公司）
Aligning Human Sense: Calibrated Distributional Reward Learning for Video Generation
City University of Hong Kong（香港城市大学）；ShanghaiTech University（上海科技大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；The Ohio State University（俄亥俄州立大学）；University of Glasgow（格拉斯哥大学）；Columbia University（哥伦比亚大学）；University of Arizona（亚利桑那大学）；GMI Cloud；National University of Singapore（新加坡国立大学）；DeciLix Lab
GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection
China Telecom Corporation Limited Wenzhou Branch（中国电信有限公司温州支公司）；Faculty of Electrical Engineering and Computer Science, Ningbo University（宁波大学电子工程与计算机科学学院）
Preference Isolation Forest for Structure-based Anomaly Detection
Department of Electronics, Information and Bioengineering, Politecnico di Milano（电子、信息与生物工程系，米兰理工大学）；Faculty of Informatics, Università della Svizzera Italiana（信息技术学院，意大利瑞士大学）
