On the Limitations of Vision-Language Models in Understanding Image Transforms

Ahmad Mustafa Anis, Hasnain Ali, Saquib Sarfraz

On the Limitations of Vision-Language Models in Understanding Image Transforms: 10 upvotes on Hugging Face Daily Papers, #24 of 39 papers on 2025-03-14. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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