Summary of Physics Context Builders: a Modular Framework For Physical Reasoning in Vision-language Models, by Vahid Balazadeh et al.
Physics Context Builders: A Modular Framework for Physical Reasoning in Vision-Language Models
by Vahid Balazadeh, Mohammadmehdi Ataei, Hyunmin Cheong, Amir Hosein Khasahmadi, Rahul G. Krishnan
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers tackle the challenge of physical reasoning for Vision-Language Models (VLMs). They show that continual fine-tuning can improve physical reasoning capabilities but note that it’s impractical to repeatedly perform fine-tuning for every task. To address this limitation, they introduce Physics Context Builders (PCBs), a novel framework that allows specialized VLMs to generate detailed physical scene descriptions. These context builders enable the separation of visual perception from reasoning, allowing analysis of their relative contributions to physical understanding. Experiments on CLEVRER and Falling Tower demonstrate that PCBs provide significant performance improvements, increasing average accuracy by up to 13.8% on complex physical reasoning tasks. Notably, PCBs show strong Sim2Real transfer, successfully generalizing from simulated training data to real-world scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to make Vision-Language Models better at understanding the world around them. They found that these models can learn by being fine-tuned over and over again, but this process is too expensive and time-consuming. So, they created a new way of teaching these models about physical reasoning called Physics Context Builders (PCBs). PCBs are like special helpers that give the models more information about what’s happening in a scene. This makes it easier for them to understand physical things like objects falling or towers collapsing. The researchers tested their idea on some difficult tasks and found that it worked really well, even when they used real-world pictures instead of just computer-generated ones. |
Keywords
» Artificial intelligence » Fine tuning
