Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters

arXiv:2403.02677v1 Announce Type: new
Abstract: We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g., CLIPScore) via integrating the recent advances in MLMs. We design four distinct yet complementary metrics to holistically measure the quality of image-text data. A new pipeline is established to construct high-quality instruction data for fine-tuning MLMs as data filters. Comparing with CLIPScore, our MLM filters produce more precise and comprehensive scores that …

abstract advances arxiv cs.cl cs.cv data design filtering filters framework image language language models metrics multimodal novel quality text type via