标题： 面向对象的自改进偏好优化用于文本到图像生成 显示英文标题

标题： Object-centric Self-improving Preference Optimization for Text-to-Image Generation

摘要： 多模态大型语言模型（MLLMs）的最新进展显著提升了图像理解和生成能力。尽管如此，MLLMs 在细粒度视觉理解方面仍面临挑战，尤其是在文本到图像生成任务中。虽然已经探索了偏好优化方法来解决图像理解任务中的这些局限性，但将其应用于图像生成领域仍缺乏深入研究。 为填补这一空白，我们提出了一种面向 MLLMs 文本到图像生成任务的对象中心自改进偏好优化（OSPO）框架。OSPO 利用了 MLLMs 的内在推理能力，而无需依赖任何外部数据集或模型。OSPO 强调高质量偏好对数据的重要性，这对有效的偏好优化至关重要。为此，它引入了一种自改进机制，通过对象中心提示扰动、密集化和 VQA 评分自主构建对象级对比偏好对。此过程消除了通常存在于朴素生成的偏好对中的模糊或不成比例的变化，从而提高了偏好优化的有效性。 我们在三个具有代表性的组合文本到图像基准上验证了 OSPO，结果显示其相对于基线模型取得了显著的性能提升。 显示英文摘要

摘要： Recent advancements in Multimodal Large Language Models (MLLMs) have significantly improved both image understanding and generation capabilities. Despite these improvements, MLLMs still struggle with fine-grained visual comprehension, particularly in text-to-image generation tasks. While preference optimization methods have been explored to address these limitations in image understanding tasks, their application to image generation remains largely underexplored. To address this gap, we propose an Object-centric Self-improving Preference Optimization (OSPO) framework designed for text-to-image generation by MLLMs. OSPO leverages the intrinsic reasoning abilities of MLLMs without requiring any external datasets or models. OSPO emphasizes the importance of high-quality preference pair data, which is critical for effective preference optimization. To achieve this, it introduces a self-improving mechanism that autonomously constructs object-level contrastive preference pairs through object-centric prompt perturbation, densification and VQA scoring. This process eliminates ambiguous or disproportionate variations commonly found in naively generated preference pairs, thereby enhancing the effectiveness of preference optimization. We validate OSPO on three representative compositional text-to-image benchmarks, demonstrating substantial performance gains over baseline models.