CLIP is a widely used foundational vision-language model that is used for
zero-shot image recognition and other image-text alignment tasks. We
demonstrate that CLIP is vulnerable to change in image quality under
compression. This surprising result is further analysed using an attribution
method-Integrated Gradients. Using this attribution method, we are able to
better understand both quantitatively and qualitatively exactly the nature in
which the compression affects the zero-shot recognition accuracy of this model.
We evaluate this extensively on CIFAR-10 and STL-10. Our work provides the
basis to understand this vulnerability of CLIP and can help us develop more
effective methods to improve the robustness of CLIP and other vision-language
models.

通过分析 CLIP 模型的脆弱性，我们揭示了其中关于图像质量和压缩对零样本图像识别准确性影响的本质，并在 CIFAR-10 和 STL-10 数据集上进行了广泛评估，从而为改进 CLIP 和其他视觉语言模型的鲁棒性提供了基础。

理解 CLIP 对图像压缩的脆弱性

Understanding the Vulnerability of CLIP to Image Compression

Recent advances in zero-shot image recognition suggest that vision-language
models learn generic visual representations with a high degree of semantic
information that may be arbitrarily probed with natural language phrases.
Understanding an image, however, is not just about understanding what content
resides within an image, but importantly, where that content resides. In this
work we examine how well vision-language models are able to understand where
objects reside within an image and group together visually related parts of the
imagery. We demonstrate how contemporary vision and language representation
learning models based on contrastive losses and large web-based data capture
limited object localization information. We propose a minimal set of
modifications that results in models that uniquely learn both semantic and
spatial information. We measure this performance in terms of zero-shot image
recognition, unsupervised bottom-up and top-down semantic segmentations, as
well as robustness analyses. We find that the resulting model achieves
state-of-the-art results in terms of unsupervised segmentation, and demonstrate
that the learned representations are uniquely robust to spurious correlations
in datasets designed to probe the causal behavior of vision models.

本篇论文研究视觉 - 语言模型在理解图像中物体所在区域和组合视觉相关部分方面的表现，提出一种最小的修改方案，获得了先进的无监督分割结果和对冗余相关性的鲁棒性。