Summary of Marvel: Multidimensional Abstraction and Reasoning Through Visual Evaluation and Learning, by Yifan Jiang et al.
MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning
by Yifan Jiang, Jiarui Zhang, Kexuan Sun, Zhivar Sourati, Kian Ahrabian, Kaixin Ma, Filip Ilievski, Jay Pujara
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 paper investigates whether multi-modal large language models (MLLMs) possess abstract visual reasoning abilities, a question that has remained open despite their significant progress on popular visual reasoning benchmarks. The researchers introduce MARVEL, a multidimensional benchmark with 770 puzzles composed of various patterns, shapes, and task configurations to comprehensively evaluate MLLMs’ reasoning abilities. They conduct experiments with nine representative MLLMs in zero-shot and few-shot settings, revealing that all models show near-random performance on the abstract visual reasoning question. Further analysis suggests that MLLMs struggle to comprehend visual features and even count panels in the puzzle, hindering their ability for abstract reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper asks if big computers can really think about pictures. They make a special test with lots of puzzles to see how good these computers are at solving them. The puzzles have shapes and patterns that need to be figured out, kind of like doing math problems. The researchers want to know if the computers can solve these puzzles on their own without being shown examples first. They tried it with nine different computer models and found that they all did pretty poorly. It seems that the computers are good at looking at pictures, but they have trouble understanding what’s going on in them. |
Keywords
» Artificial intelligence » Few shot » Multi modal » Zero shot
