对比揭示共性：通过对比反演进行定制化图像生成
Comparison Reveals Commonality: Customized Image Generation through Contrastive Inversion
AI总结：
本文提出Contrastive Inversion方法，通过对比学习比较输入图像提取共性概念，结合解耦交叉注意力微调提升概念保真度，在概念表示与编辑任务上优于现有技术。
摘要：
近期对定制化图像生成的需求引发了对从小规模图像集中有效提取共性概念技术的需求。现有方法通常依赖额外指导（如文本提示或空间掩码）来捕获目标共性概念。然而，依赖人工提供的指导会导致辅助特征分离不彻底，从而降低生成质量。本文提出Contrastive Inversion，一种通过比较输入图像来识别共性概念且不依赖额外信息的新颖方法。我们通过对比学习训练目标token及图像级辅助文本token，从而提取目标充分解耦的真实语义。随后应用解耦交叉注意力微调，在不过拟合的前提下提升概念保真度。实验结果与分析表明，该方法在概念表示与编辑两方面均实现了均衡的高水平性能，优于现有技术。
英文摘要：
The recent demand for customized image generation raises a need for techniques that effectively extract the common concept from small sets of images. Existing methods typically rely on additional guidance, such as text prompts or spatial masks, to capture the common target concept. Unfortunately, relying on manually provided guidance can lead to incomplete separation of auxiliary features, which degrades generation quality.In this paper, we propose Contrastive Inversion, a novel approach that identifies the common concept by comparing the input images without relying on additional information. We train the target token along with the image-wise auxiliary text tokens via contrastive learning, which extracts the well-disentangled true semantics of the target. Then we apply disentangled cross-attention fine-tuning to improve concept fidelity without overfitting. Experimental results and analysis demonstrate that our method achieves a balanced, high-level performance in both concept representation and editing, outperforming existing techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
