Stable Attribute Group Editing for Reliable Few-Shot Image Generation
文献类型：期刊论文
| 作者 | Ding, Guanqi2,3; Han, Xinzhe1; Wang, Shuhui3,4; Jin, Xin5; Huang, Qingming2,3,4 |
| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 35期号:12页码:12719-12733 |
| 关键词 | Image synthesis Training Diffusion models Dictionaries Generators Data models Codes Visualization Reliability Analytical models Few-shot image generation generative adversarial network image editing |
| ISSN号 | 1051-8215 |
| DOI | 10.1109/TCSVT.2025.3578670 |
| 英文摘要 | Few-shot image generation aims to generate data of an unseen category based on only a few samples. Apart from basic content generation, a bunch of downstream applications hopefully benefit from this task, such as low-data detection and few-shot classification. To achieve this goal, the generated images should guarantee category retention for classification beyond the visual quality and diversity. In our preliminary work, we present an "editing-based" framework, Attribute Group Editing (AGE), for reliable few-shot image generation, which largely improves the performance compared with existing methods that require re-training a GAN with limited data. Nevertheless, AGE's performance on downstream classification is not as satisfactory as expected. Furthermore, existing generative models suffer from similar issues. This paper focuses on addressing the issue of universal class inconsistency in all generative models. It not only improves AGE to enhance its ability to preserve class information but also conducts a comprehensive analysis of the causes of this problem in generative models from multiple perspectives, proposing potential directions for resolution. We first propose Stable Attribute Group Editing (SAGE) for more stable class-relevant image generation. SAGE corrects the inaccurate assumptions in AGE and leverages the distribution information from seen categories to accurately estimate the data distribution of unseen categories, thereby eliminating the class inconsistency issue in the generated data. We apply SAGE to both GANs and diffusion models to verify its flexibility and further achieve promising generation performance. Going one step further, we find that even though the generated images look photo-realistic and require no category-relevant editing, they are usually of limited help for downstream classification. We systematically discuss this issue from both the generation and classification perspectives, and propose to boost the downstream classification performance of SAGE by enhancing the pixel and frequency components. Extensive experiments provide valuable insights into extending image generation to wider downstream applications. Codes are available at https://github.com/UniBester/SAGE |
| 资助项目 | National Key Research and Development Program of China[2023YFC2508704] ; National Natural Science Foundation of China[62022083] ; National Natural Science Foundation of China[62236008] ; Fundamental Research Funds for the Central Universities |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001631874000018 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42983]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Wang, Shuhui |
| 作者单位 | 1.China Acad Aerosp Syst & Innovat CASI, China Aerosp Sci & Technol Corperat CASC, Beijing 100088, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Peng Cheng Lab, Shenzhen 518066, Peoples R China 5.Huawei Cloud EI Innovat Lab, Beijing 100085, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ding, Guanqi,Han, Xinzhe,Wang, Shuhui,et al. Stable Attribute Group Editing for Reliable Few-Shot Image Generation[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2025,35(12):12719-12733. |
| APA | Ding, Guanqi,Han, Xinzhe,Wang, Shuhui,Jin, Xin,&Huang, Qingming.(2025).Stable Attribute Group Editing for Reliable Few-Shot Image Generation.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,35(12),12719-12733. |
| MLA | Ding, Guanqi,et al."Stable Attribute Group Editing for Reliable Few-Shot Image Generation".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35.12(2025):12719-12733. |
入库方式： OAI收割
来源：计算技术研究所
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