Summary of Zero-shot Visual Reasoning by Vision-language Models: Benchmarking and Analysis, By Aishik Nagar et al.
Zero-Shot Visual Reasoning by Vision-Language Models: Benchmarking and Analysis
by Aishik Nagar, Shantanu Jaiswal, Cheston Tan
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 This research paper investigates the capabilities of vision-language models (VLMs) in real-world visual question answering (VQA) tasks. Despite impressive performance on benchmarks, it remains unclear whether VLMs’ abilities are driven by their world knowledge or actual visual reasoning skills. The authors argue that current benchmarks conflate pure visual reasoning with world knowledge, and questions often involve a limited number of reasoning steps. To better understand VLMs’ capabilities, this study aims to decouple these factors and evaluate the models’ performance on more nuanced tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computers can answer questions about pictures using only a few words or no words at all. Computers have gotten very good at doing this, but it’s not clear if they’re really understanding what they’re seeing, or just relying on what they’ve learned from text. The researchers want to figure out which one is true, and are trying to design new tests that will help them find out. |
Keywords
* Artificial intelligence * Question answering
