Summary of Sat: Spatial Aptitude Training For Multimodal Language Models, by Arijit Ray et al.
SAT: Spatial Aptitude Training for Multimodal Language Models
by Arijit Ray, Jiafei Duan, Reuben Tan, Dina Bashkirova, Rose Hendrix, Kiana Ehsani, Aniruddha Kembhavi, Bryan A. Plummer, Ranjay Krishna, Kuo-Hao Zeng, Kate Saenko
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Robotics (cs.RO)
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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 focuses on improving spatial intelligence in large multimodal language models (MLMs). Current studies have shown that MLMs struggle with static spatial reasoning, such as categorizing object positions. However, real-world applications require dynamic capabilities like perspective-taking and egocentric action recognition. To address this, the authors introduce Spatial Aptitude Training (SAT), a dataset that goes beyond static questions to include more dynamic tasks. SAT contains 218K question-answer pairs for 22K synthetic scenes across a training and testing set. The authors find that even MLMs that perform well on static questions struggle with dynamic spatial questions. Furthermore, instruction-tuning data improves not only dynamic spatial reasoning on SAT but also zero-shot performance on existing real-image spatial benchmarks like CVBench, BLINK, and VSR. The authors’ 13B model matches larger proprietary MLMs in spatial reasoning when instruction-tuned on SAT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching computers to better understand spatial concepts. Currently, large computer programs struggle with understanding how objects relate to each other in space. But real-world applications require more than just understanding static object positions – they need computers that can imagine themselves in different scenarios and take actions accordingly. To help computers learn this skill, the authors created a new dataset called Spatial Aptitude Training (SAT). SAT includes many question-answer pairs for 22,000 synthetic scenes. The results show that even large computer programs struggle with dynamic spatial questions. But by providing special training data, these programs can improve their spatial reasoning skills and perform better on tests. |
Keywords
» Artificial intelligence » Instruction tuning » Zero shot
