Summary of Unibench: Visual Reasoning Requires Rethinking Vision-language Beyond Scaling, by Haider Al-tahan et al.
UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
by Haider Al-Tahan, Quentin Garrido, Randall Balestriero, Diane Bouchacourt, Caner Hazirbas, Mark Ibrahim
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces UniBench, a unified implementation of over 50 vision-language model (VLM) benchmarks that span a range of capabilities from object recognition to spatial awareness and counting. The authors showcase the utility of UniBench by evaluating nearly 60 publicly available VLMs, trained on up to 12.8 billion samples. They find that scaling training data or model size can improve many VLM capabilities, but offers little benefit for reasoning or relations. Surprisingly, they also discover that today’s best VLMs struggle with simple digit recognition and counting tasks like MNIST, which simpler networks can solve. The authors propose more precise interventions like data quality or tailored-learning objectives to overcome these limitations. For practitioners, they offer guidance on selecting a suitable VLM for a given application. The paper releases an easy-to-run UniBench code-base with the full set of benchmarks and comparisons across 59 models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for researchers to compare and improve vision-language models (VLMs). Right now, it’s hard to figure out which VLM is best because there are many different tests or “benchmarks” that measure different things. The authors create a single tool called UniBench that includes all these benchmarks, so scientists can easily see how different VLMs perform on each one. They tested 60 of the most popular VLMs and found some surprising results. For example, even the best VLMs have trouble recognizing simple numbers and counting objects. The authors suggest ways to make VLMs better at these tasks and provide advice for scientists who want to choose the right VLM for a specific project. |
Keywords
* Artificial intelligence * Language model
