On the Limitations of Vision-Language Models in Understanding Image
  Transforms
Authors: name: Ahmad Mustafa Anis, name: Hasnain Ali, name: Saquib Sarfraz

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.