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

• URL: http://arxiv.org/abs/2503.09837v2
• Date: Fri, 14 Mar 2025 01:44:17 GMT
• Title: On the Limitations of Vision-Language Models in Understanding Image Transforms
• Authors: Ahmad Mustafa Anis, Hasnain Ali, Saquib Sarfraz,
• Abstract summary: Vision Language Models (VLMs) have demonstrated significant potential in various downstream tasks.<n>This paper investigates the image-level understanding of VLMs, specifically CLIP by OpenAI and SigLIP by Google.<n>Our findings reveal that these models lack comprehension of multiple image-level augmentations.
• Score: 0.9217021281095907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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