On the Difference of BERT-style and CLIP-style Text Encoders
Zhihong Chen, Guiming Chen, Shizhe Diao, Xiang Wan, Benyou Wang · 2023
Masked language modeling (MLM) has been one of the most popular pretraining recipes in natural language processing, e.g., BERT, one of the representative models.Recently, contrastive language-image pretraining (CLIP) has also attracted attention, especially its vision models that achieve excellent performance on a broad range of vision tasks.However, few studies are dedicated to studying the text encoders learned by CLIP.In this paper, we analyze the difference between BERT-style and CLIP-style text encoders from three experiments: (i) general text understanding, (ii) vision-centric text understanding, and (iii) text-to-image generation.Experimental analyses show that although CLIP-style text encoders underperform BERTstyle ones for general text understanding tasks, they are equipped with a unique ability, i.e., synesthesia, for the cross-modal association, which is more similar to the senses of humans.Our code is released at https://github.com/ zhjohnchan/probing-clip-dev.