TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives
This addresses a bottleneck in vision-language models for tasks requiring compositional understanding, offering a practical enhancement with incremental improvements.
The paper tackled the problem of limited compositional reasoning in CLIP models due to insufficient diversity in training data, and by generating synthetic hard negatives, it achieved over 9% absolute improvement on the SugarCrepe benchmark while maintaining computational efficiency.
Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between text and visual modalities to learn representations. This makes the nature of the training data a significant factor in the efficacy of CLIP for downstream tasks. However, the lack of compositional diversity in contemporary image-text datasets limits the compositional reasoning ability of CLIP. We show that generating ``hard'' negative captions via in-context learning and synthesizing corresponding negative images with text-to-image generators offers a solution. We introduce a novel contrastive pre-training strategy that leverages these hard negative captions and images in an alternating fashion to train CLIP. We demonstrate that our method, named TripletCLIP, when applied to existing datasets such as CC3M and CC12M, enhances the compositional capabilities of CLIP, resulting in an absolute improvement of over 9% on the SugarCrepe benchmark on an equal computational budget, as well as improvements in zero-shot image classification and image retrieval. Our code, models, and data are available at: https://tripletclip.github.io