Summary of What Makes a Maze Look Like a Maze?, by Joy Hsu et al.
What Makes a Maze Look Like a Maze?
by Joy Hsu, Jiayuan Mao, Joshua B. Tenenbaum, Noah D. Goodman, Jiajun Wu
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 aspect of human visual comprehension is the ability to flexibly interpret abstract concepts: extracting underlying rules, grounding them across familiar and unfamiliar contexts, and making predictions or reasoning about them. While off-the-shelf vision-language models excel at literal interpretations (e.g., recognizing object categories), they struggle with visual abstractions (e.g., maze formation). To address this challenge, we introduce Deep Schema Grounding (DSG), a framework leveraging structured representations of visual abstractions for grounding and reasoning. Schemas, dependency graph descriptions of abstract concepts decomposed into primitive-level symbols, serve as the core of DSG. Large language models extract schemas, which are then hierarchically grounded onto images with vision-language models. The grounded schema augments understanding of visual abstractions. We evaluate DSG and methods on our new Visual Abstractions Dataset, consisting of diverse, real-world images and corresponding question-answer pairs labeled by humans. Results show that DSG significantly improves abstract visual reasoning performance of vision-language models, marking a step toward human-aligned understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can understand abstract concepts in pictures. Right now, computers are good at recognizing objects like trees or animals, but they struggle to understand more complex things like shapes or patterns. The researchers created a new way to help computers understand these kinds of things by using special rules and images. They tested this method on a big collection of pictures and questions, and it worked really well! This is important because it could help computers become better at understanding the world around them. |
Keywords
» Artificial intelligence » Grounding
