Vision Language Models (VLMs) have demonstrated significant potential in various downstream tasks, including Image/Video Generation, Visual Question Answering, Multimodal Chatbots, and Video Understanding. However, these models often struggle with basic image transformations. This paper investigates the image-level understanding of VLMs, specifically CLIP by OpenAI and SigLIP by Google. Our findings reveal that these models lack comprehension of multiple image-level augmentations. To facilitate this study, we created an augmented version of the Flickr8k dataset, pairing each image with a detailed description of the applied transformation. We further explore how this deficiency impacts downstream tasks, particularly in image editing, and evaluate the performance of state-of-the-art Image2Image models on simple transformations.
@article{arxiv.2503.09837,
title = {On the Limitations of Vision-Language Models in Understanding Image Transforms},
author = {Ahmad Mustafa Anis and Hasnain Ali and Saquib Sarfraz},
journal= {arXiv preprint arXiv:2503.09837},
year = {2025}
}