Role-SynthCLIP：一种角色扮演驱动的多样化合成数据方法
Role-SynthCLIP: A Role-Play Driven Diverse Synthetic Data Approach
用多视角角色扮演提示驱动 MLLM 为同一张图生成互补字幕，在固定图像数量下提升图文对的语义多样性，可直接复用于 CLIP 类模型的预训练数据构造与细粒度对齐。
原文依据 leverages multi-perspective role-playing prompts (e.g., a compositional analyst, an interpreter of image context) to guide Multimodal Large Language Models (MLLMs)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论对比语言-图像预训练（CLIP）模型的有效性关键取决于其训练数据的语义多样性与质量。然而，现有合成数据生成方法主要着眼于增加数据量，这种侧重往往导致语义多样性受限、字幕冗余或流于浅表。为缓解这一局限，我们提出 Role-SynthCLIP，一种新的数据合成框架，它利用多视角角色扮演提示（例如一位构图分析师、一位图像语境解读者）引导多模态大语言模型（MLLM）从不同视角生成语义多样的字幕。该机制在保持训练图像数量不变的前提下，通过为每张图像补充多条互补字幕，提升了合成图文对的语义多样性与细粒度图文对齐。实验结果表明了本方法的有效性与高效性。仅在 100 万张图像上训练的 CLIP-B/16 模型在 MS COCO 验证集上取得 64.1% 的 Recall@1，超过现有最佳合成数据基线（在 500 万对数据上训练）2.8 个百分点。代码与训练好的模型见 https://github.com/huangfu170/Role-SynthCLIP。
英文原文摘要 1274 字符
The effectiveness of Contrastive Language-Image Pre-training (CLIP) models critically depends on the semantic diversity and quality of their training data. However, while existing synthetic data generation methods primarily focus on increasing data volume, such emphasis often leads to limited semantic diversity and redundant or shallow captions. To address this limitation, we propose Role-SynthCLIP, a novel data synthesis framework that leverages multi-perspective role-playing prompts (e.g., a compositional analyst, an interpreter of image context) to guide Multimodal Large Language Models (MLLMs) in generating semantically diverse captions from distinct viewpoints. This mechanism enhances the semantic diversity and fine-grained image-text alignment of synthetic pairs by enriching each image with multiple complementary captions, while keeping the number of training images fixed. Experimental results demonstrate the effectiveness and efficiency of our method. A CLIP-B/16 model trained on only 1 million images achieves a Recall@1 of 64.1% on the MS COCO validation set, surpassing the best existing synthetic data baseline (trained on 5M pairs) by 2.8 percentage points. The code and trained models can be found at https://github.com/huangfu170/Role-SynthCLIP.