AgentComp: 从代理推理到文本-图像模型中的组合性 mastery
AgentComp: From Agentic Reasoning to Compositional Mastery in Text-to-Image Models
AI总结：
AgentComp通过训练模型区分组合性相似的提示和图像，提升文本-图像模型的组合性生成能力，实现更准确的图像生成。
摘要：
文本-图像生成模型在视觉质量上取得了显著成就，但仍在组合性方面存在困难——准确捕捉提示中的对象关系、属性绑定和细粒度细节。一个关键限制是模型没有被显式训练来区分组合性相似的提示和图像，导致输出接近预期描述但在细粒度细节上偏离。为此，我们提出了AgentComp，一个框架，专门训练模型更好地区分此类组合性变化并增强其推理能力。AgentComp利用配备了图像生成、编辑和视觉问答工具的大型语言模型的推理和工具使用能力，自主构建组合性数据集。利用这些数据集，我们应用一种代理偏好优化方法对文本-图像模型进行微调，使它们能够更好地区分组合性相似的样本，从而在整体上具有更强的组合性生成能力。AgentComp在组合性基准如T2I-CompBench上实现了最先进的结果，不妥协图像质量——这是先前方法的常见缺点——并且甚至能推广到其他未显式训练的能力，如文本渲染。
英文摘要：
Text-to-image generative models have achieved remarkable visual quality but still struggle with compositionality$-$accurately capturing object relationships, attribute bindings, and fine-grained details in prompts. A key limitation is that models are not explicitly trained to differentiate between compositionally similar prompts and images, resulting in outputs that are close to the intended description yet deviate in fine-grained details. To address this, we propose AgentComp, a framework that explicitly trains models to better differentiate such compositional variations and enhance their reasoning ability. AgentComp leverages the reasoning and tool-use capabilities of large language models equipped with image generation, editing, and VQA tools to autonomously construct compositional datasets. Using these datasets, we apply an agentic preference optimization method to fine-tune text-to-image models, enabling them to better distinguish between compositionally similar samples and resulting in overall stronger compositional generation ability. AgentComp achieves state-of-the-art results on compositionality benchmarks such as T2I-CompBench, without compromising image quality$-$a common drawback in prior approaches$-$and even generalizes to other capabilities not explicitly trained for, such as text rendering.
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。
