Summary of Vismin: Visual Minimal-change Understanding, by Rabiul Awal et al.
VisMin: Visual Minimal-Change Understanding
by Rabiul Awal, Saba Ahmadi, Le Zhang, Aishwarya Agrawal
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel benchmark is introduced to assess visual-language models’ (VLMs) ability to understand fine-grained object attributes, relationships, and minimal changes between images and captions. The Visual Minimal-Change Understanding (VisMin) benchmark requires models to predict correct image-caption matches given two images and two captions with minimal changes in objects, attributes, counts, or spatial relations. Current VLMs are found to be deficient in understanding spatial relationships and counting abilities, but a large-scale training dataset can improve fine-grained understanding across benchmarks and in CLIP’s general image-text alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VisMin is a new benchmark that tests how well AI models understand objects, attributes, and relationships between them. It looks at small changes in images and captions, like changing the color or shape of an object, to see if the model can still recognize it. Current models are not very good at understanding these kinds of changes, but by training them on a big dataset, we can make them better. |
Keywords
» Artificial intelligence » Alignment
