# Augmenting CLIP with Improved Visio-Linguistic Reasoning

**Authors:** Samyadeep Basu, Maziar Sanjabi, Daniela Massiceti, Shell Xu Hu, Soheil Feizi  
**arXiv:** [2307.09233](https://arxiv.org/abs/2307.09233)  
**Date:** 2023-07-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.09233v1/

## Key points

- Authors address limitations of image-text contrastive models like CLIP in compositional visio-linguistic tasks
- Proposed method called SDS-CLIP to overcome these limitations
- Core idea is to use differentiable image parameterizations to fine-tune CLIP with a distillation objective from large text-to-image generative models
- Evaluations on Winoground and ARO datasets show significant improvements in visio-linguistic performance (up to 7% on Winoground and up to 3% on ARO)
- Marginal improvements observed in zero-shot performance on downstream datasets
- Study emphasizes the importance of carefully designed distillation objectives from generative models for enhancing visio-linguistic reasoning capabilities
- Proposed method offers a promising solution for improving performance of CLIP and similar vision language models in compositional reasoning tasks.

## Summary

In the paper titled "Augmenting CLIP with Improved Visio-Linguistic Reasoning," the authors address the limitations of image-text contrastive models, such as CLIP, in compositional visio-linguistic tasks. While these models are effective for zero-shot classification, image-text retrieval and transfer learning, they often fail to perform well on tasks that require complex visio-linguistic reasoning. To overcome this issue, the authors propose a sample-efficient and lightweight method called SDS-CLIP. The core idea of their approach is to use differentiable image parameterizations to fine-tune CLIP with a distillation objective from large text-to-image generative models like Stable Diffusion. These generative models excel at visio-linguistic reasoning tasks. The authors evaluate their method on challenging benchmarks like Winoground and ARO datasets. They find that SDS-CLIP significantly improves the absolute visio-linguistic performance of different CLIP models by up to 7% on Winoground and up to 3% on ARO. Additionally, they observe marginal improvements in zero shot performance on various downstream datasets. The study highlights the importance of carefully designed distillation objectives from generative models in extending existing contrastive image text models with enhanced visio linguistic reasoning capabilities. The proposed method offers a promising solution for improving the performance of CLIP and similar vision language models in compositional reasoning tasks.