Role-SynthCLIP: A Role Play Driven Diverse Synthetic Data Approach
This addresses the need for more effective synthetic data generation in multimodal AI, though it is incremental as it builds on existing CLIP and synthetic data methods.
The paper tackles the problem of limited semantic diversity in synthetic data for CLIP models by proposing Role-SynthCLIP, a framework that uses role-playing prompts to generate diverse captions, resulting in a CLIP-B/16 model trained on 1 million pairs achieving 64.1% Recall@1 on MS COCO, outperforming a baseline trained on 5 million pairs by 2.8 percentage points.
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, thereby improving caption expressiveness and accuracy while keeping the total number of image-text pairs unchanged. Experimental results demonstrate the effectiveness and efficiency of our method. A CLIP-B/16 model trained on only 1 million Role-SynthCLIP pairs 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 are released at https://github.com/huangfu170/Role-SynthCLIP.