用大模型先验引导跨模态表示，提升细粒度检索效果
Guiding Cross-Modal Representations with MLLM Priors via Preference Alignment
- 利用现成多模态大模型生成偏好数据，指导嵌入学习
- 在COCO、Flickr30K上相比CLIP提升12.3%和9.8%的检索准确率
- 适合需要精准图文匹配的视觉语言任务研究者
尽管对比语言-图像预训练（CLIP）在跨模态内容检索方面表现出色，但其特征空间中仍存在显著的模态差距。我们发现，现成的多模态大语言模型（MLLM）具有强大的内在模态对齐能力。尽管近期基于MLLM的统一架构检索器部分缓解了这一问题，但其依赖粗粒度对齐机制的本质限制了潜力。本文提出MAPLE（Modality-Aligned Preference Learning for Embeddings），一种利用MLLM内在细粒度对齐先验引导跨模态表示学习的新框架。MAPLE将学习过程建模为强化学习，包含两个关键组件：(1) 利用现成MLLM自动构建偏好数据；(2) 提出新的相对偏好对齐（RPA）损失，将直接偏好优化（DPO）适配到嵌入学习场景。实验表明，该偏好引导对齐在细粒度跨模态检索中取得显著提升，验证了其处理细微语义差异的有效性。
原文摘要 · Abstract (English)
Despite Contrastive Language-Image Pretraining (CLIP)'s remarkable capability to retrieve content across modalities, a substantial modality gap persists in its feature space. Intriguingly, we discover that off-the-shelf MLLMs (Multimodal Large Language Models) demonstrate powerful inherent modality alignment properties. While recent MLLM-based retrievers with unified architectures partially mitigate this gap, their reliance on coarse modality alignment mechanisms fundamentally limits their potential. In this work, We introduce MAPLE (Modality-Aligned Preference Learning for Embeddings), a novel framework that leverages the fine grained alignment priors inherent in MLLM to guide cross modal representation learning. MAPLE formulates the learning process as reinforcement learning with two key components: (1) Automatic preference data construction using off-the-shelf MLLM, and (2) a new Relative Preference Alignment (RPA) loss, which adapts Direct Preference Optimization (DPO) to the embedding learning setting. Experimental results show that our preference-guided alignment achieves substantial gains in fine-grained cross-modal retrieval, underscoring its effectiveness in handling nuanced semantic distinctions.
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