Physics Context Builders: A Modular Framework for Physical Reasoning in Vision-Language Models
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911237914230784 |
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| author | Balazadeh, Vahid Ataei, Mohammadmehdi Cheong, Hyunmin Khasahmadi, Amir Hosein Krishnan, Rahul G. |
| author_facet | Balazadeh, Vahid Ataei, Mohammadmehdi Cheong, Hyunmin Khasahmadi, Amir Hosein Krishnan, Rahul G. |
| contents | Physical reasoning remains a significant challenge for Vision-Language Models (VLMs). This limitation arises from an inability to translate learned knowledge into predictions about physical behavior. Although continual fine-tuning can mitigate this issue, it is expensive for large models and impractical to perform repeatedly for every task. This necessitates the creation of modular and scalable ways to teach VLMs about physical reasoning. To that end, we introduce Physics Context Builders (PCBs), a modular framework where specialized smaller VLMs are fine-tuned to generate detailed physical scene descriptions. These can be used as physical contexts to enhance the reasoning capabilities of larger VLMs. PCBs enable the separation of visual perception from reasoning, allowing us to analyze their relative contributions to physical understanding. We perform experiments on CLEVRER and on Falling Tower, a stability detection dataset with both simulated and real-world scenes, to demonstrate that PCBs provide substantial performance improvements, increasing average accuracy by up to 13.8% on complex physical reasoning tasks. Notably, PCBs also show strong Sim2Real transfer, successfully generalizing from simulated training data to real-world scenes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_08619 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics Context Builders: A Modular Framework for Physical Reasoning in Vision-Language Models Balazadeh, Vahid Ataei, Mohammadmehdi Cheong, Hyunmin Khasahmadi, Amir Hosein Krishnan, Rahul G. Computer Vision and Pattern Recognition Artificial Intelligence Physical reasoning remains a significant challenge for Vision-Language Models (VLMs). This limitation arises from an inability to translate learned knowledge into predictions about physical behavior. Although continual fine-tuning can mitigate this issue, it is expensive for large models and impractical to perform repeatedly for every task. This necessitates the creation of modular and scalable ways to teach VLMs about physical reasoning. To that end, we introduce Physics Context Builders (PCBs), a modular framework where specialized smaller VLMs are fine-tuned to generate detailed physical scene descriptions. These can be used as physical contexts to enhance the reasoning capabilities of larger VLMs. PCBs enable the separation of visual perception from reasoning, allowing us to analyze their relative contributions to physical understanding. We perform experiments on CLEVRER and on Falling Tower, a stability detection dataset with both simulated and real-world scenes, to demonstrate that PCBs provide substantial performance improvements, increasing average accuracy by up to 13.8% on complex physical reasoning tasks. Notably, PCBs also show strong Sim2Real transfer, successfully generalizing from simulated training data to real-world scenes. |
| title | Physics Context Builders: A Modular Framework for Physical Reasoning in Vision-Language Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.08619 |