Diffusion-Based Image Generation

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Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification
Jul 14, 2026Jeeyung Kim, Erfan Esmaeili, Qiang QiuSynthetic Data Augmentation Diffusion Model Fine-Tuning
Purdue University, West Lafayette, USA
MetaView: Monocular Novel View Synthesis with Scale-Aware Implicit Geometry Priors
Jul 13, 2026Yufei Cai, Xuesong Niu, Hao Lu +3Multi-View Consistency Novel View Synthesis
Nanyang Technological University · Kolors Team, Kuaishou Technology · The Hong Kong University of Science and Technology (Guangzhou)
An Hybrid Quantum-Classical Diffusion Model for Image Generation
Jul 8, 2026Qipeng Qian, Keli Deng, Yuntao QianLatent Diffusion Models Quantum Generative Modeling
Program of Applied Mathematics, Department of Mathematics, University of Arizona, Tucson, USA · College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Multi-Conditioned Diffusion Synthesis of Sand Boils for Low-Resource Earthen-Levee Inspection
Jul 8, 2026Padam Jung Thapa, Abdullah Bin Naeem, Ayon Dey +2Synthetic Data Augmentation Conditional Image Generation
University of Louisiana at Lafayette, Lafayette, LA, USA · Department of Computer Science, Louisiana State University New Orleans, New Orleans, LA, USA
Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation
Jul 3, 2026Chaofan Gan, Zicheng Zhao, Yuanpeng Tu +6Visual Representation Learning Diffusion Transformer
Shanghai Jiao Tong University, Shanghai, China · The University of Hong Kong, Hong Kong, China · Monash University, Melbourne, Australia +2
Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models
Jul 2, 2026Songping Wang, Yueming Lyu, Shiqi Liu +5Adversarial Attacks Diffusion-Based Image Generation
School of Intelligence Science and Technology, Nanjing University, Suzhou 215163, China · China Mobile Information Technology Co., Ltd., Beijing 100037, China · State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Center for Research on Intelligent Perception and Computing (CRIPAC), Institute of Automation Chinese Academy of Sciences (CASIA), Beijing 100190, China
Post-Training Pruning for Diffusion Transformers
Jul 1, 2026Chengzhi Hu, Xuewen Liu, Jing Zhang +3Diffusion Transformer Diffusion-Based Image Generation
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences
No Prompt, No Leaks: A Robust Generative Steganography Framework via Prompt-Free Diffusion
Jun 30, 2026Jingwen Cai, Fen Xiao, Shuhua Deng +1Latent Diffusion Models Diffusion-Based Image Generation
MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan 411105, China · College of Information Science and Engineering, Hunan Normal University, Changsha 410006, China
AC3S: Adaptive Conditioning for 3D-Aware Synthetic Data Generation
Jun 30, 2026Eric Ji, Qiran Hu, Wufei Ma +4Synthetic Data Generation Diffusion-Based Image Generation
University of Illinois Urbana-Champaign · Johns Hopkins University · College of Engineering and Computer Science, VinUniversity
WaterGen: Decoupling Scene and Medium in Underwater Image Generation
Jun 30, 2026Jiayi Wu, Tianfu Wang, Tianyi Xiong +6Synthetic Data Augmentation Image Generation
University of Maryland · University of South Florida · University of Florida
TerraDiT- $Ω$ : Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive
Jun 30, 2026Brian Wei, Srikumar Sastry, Daniel Cher +2Image Generation Diffusion-Based Image Generation
Washington University in St. Louis
PhotoQuilt: Training-Free Arbitrary-Resolution Photomosaics via Bootstrapped Tiled Denoising
Jun 29, 2026Koorosh Roohi, Javad Rajabi, Andrew Fleet +1Image Generation Compositional Image Generation
University of Toronto · Vector Institute · KITE Research Institute +2
OrthoTryOn: Geometric Orthogonalization for Conflict-Free Unified Fashion Generation
Jun 26, 2026Zhaotong Yang, Ying Tai, Jiahui Zhan +3Diffusion Model Guidance Virtual Try-On
PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology · PCA Lab, School of Intelligence Science and Technology, Nanjing University · Shanghai Jiao Tong University
PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion
Jun 26, 2026Zipeng Guo, Lichen Ma, Yu He +4Diffusion Transformer Pixel-Space Diffusion Models
Xi’an Jiaotong University
TMP: Tree-structured Mixed-policy Pruning for Large-scale Image Generation and Editing
Jun 25, 2026Peizhen Zhang, Yang Li, Xunsong Li +10Model Compression Diffusion-Based Image Generation
Multimodal Model Department, Tencent
Focusing on What Matters: Saliency-Harnessing Accurate Routing for Diffusion MoE
Jun 25, 2026Haoyou Deng, Keyu Yan, Chaojie Mao +4Expert Routing Diffusion-Based Image Generation
Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology · Tongyi Lab, Alibaba Group
PortraitGen: Exemplar-Driven GRPO with Dual-Reward Guidance for Photorealistic Portrait Generation
Jun 25, 2026Xiaomin Li, Qian Liang, Yinan Li +5Group Relative Policy Optimization Image Generation
Dalian University of Technology · University of Electronic Science and Technology of China · Zhejiang University +1
LearniBridge: Learnable Calibration of Feature Caching for Diffusion Models Acceleration
Jun 25, 2026Xuyue Huang, Zhe Chen, Wang Shen +1Video Diffusion Models Diffusion Transformer
Shenzhen Ubiquitous Data Enabling Key Laboratory, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Central Media Technology Institute, Huawei Technologies Co., Ltd., Shenzhen, China
TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models
Jun 24, 2026William Aiken, Paula Branco, Guy-Vincent Jourdan +1Backdoor Attacks Diffusion-Based Image Generation
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Concept Removal for Frontier Image Generative Models
Jun 24, 2026Aditya Kumar, Pierre Joly, Adam Dziedzic +1Generative Modeling Diffusion-Based Image Generation
CISPA Helmholtz Center for Information Security, Germany.
High-Fidelity Synthetic Transmission Electron Microscopy Image Generation Using Diffusion Probabilistic Models for Data-Limited Semiconductor Metrology
Jun 23, 2026Johannes Boehm, Bappaditya DeySemiconductor Manufacturing Synthetic Data Generation
Chemnitz University of Technology, Chemnitz, Germany · Interuniversity Microelectronics Centre (imec), Leuven, Belgium
Information-Theoretic Classifier-Free Guidance with Adaptive Schedule Optimization
Jun 23, 2026Haobo Chen, Xiangxiang Xu, Yuheng BuDiffusion Model Guidance Classifier-Free Guidance
Department of Computer Science University of California, Santa Barbara · Department of Computer Science University of Rochester
Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers
Jun 18, 2026Dong Hoon Lee, Seunghoon HongToken Merging Diffusion-Based Image Generation
Kim Jaechul Graduate School of AI, KAIST, Daejeon, South Korea · School of Computing, KAIST, Daejeon, South Korea
One-Shot Novel View and Pose Human Image Synthesis via 3D Prior Guided Diffusion Model
Jun 18, 2026Shenjian Gong, Kangkan Wang, Shanshan Zhang +1Image Generation Novel View Synthesis
PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, and Jiangsu Key Lab of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 China
Learning When to Denoise: Optimizing Asynchronous Schedules for Latent Diffusion
Jun 18, 2026Bingshuo Qian, Xiang ChengFlow Matching Latent Diffusion Models
Department of Electrical and Computer Engineering Duke University
The Market in the Model: Latent Diffusion as Neural Economy
Jun 17, 2026Eryk SalvaggioLatent Diffusion Models Diffusion-Based Image Generation
Cambridge Digital Humanities, University of Cambridge · Machine Visual Culture Research Group, Max Planck Institute
SpectralDiT: Timestep-Conditioned Spectral Residual Correction for Flow-Matching DiTs
Jun 17, 2026Jiayu TianFlow Matching Diffusion Transformer
Peking University
Volterra Generative Models
Jun 16, 2026Yusen Jia, Bingyan HanDiffusion Sampling Diffusion-Based Image Generation
The Hong Kong University of Science and Technology (Guangzhou)
Show the Signal, Hide the Noise: Spectral Forcing for Pixel-Space Diffusion
Jun 13, 2026Weichen Fan, Haiwen Diao, Penghao Wu +1Pixel-Space Diffusion Models Diffusion-Based Image Generation
1S-Lab, Nanyang Technological University
SmartFont: Dynamic Condition Allocation for Few-Shot Font Generation
Jun 11, 2026Zian Yang, Zixin WangFew-Shot Learning Diffusion-Based Image Generation
Fudan University

Jul 14, 2026, cs.CVJ/K move · Enter open · S save

Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification

Jeeyung Kim, Erfan Esmaeili, Qiang Qiu

Purdue University, West Lafayette, USA

Synthetic Data Augmentation Diffusion Model Fine-Tuning Diffusion-Based Image Generation

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When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task. Existing approaches largely improve synthetic data by increasing realism, diversity, or domain adaptation, while overlooking a more fundamental question: how should sample usefulness for classification be measured and optimized? We address this with Class-Contrastive Influence (C2I), a criterion that quantifies a sample's usefulness through its gradient-based influence on the classifier. We find that effective samples exhibit a strong C2I gap: their loss gradients align with validation gradients from the same class and oppose those from other classes. Our analysis further suggests that such high-C2I samples are hard, boundary-proximal examples that help refine the decision boundary and improve robustness. Building on this insight, we fine-tune diffusion models with reinforcement learning using a C2I-based reward to steer generation toward class-informative samples. Across several few-shot medical imaging benchmarks, C2I-guided generation improves downstream accuracy and robustness over diffusion-based augmentation baselines, showing that synthetic augmentation is most effective when guided by task usefulness rather than image quality alone.

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