Embodied Scene Understanding for Vision Language Models via MetaVQA

• URL: http://arxiv.org/abs/2501.09167v1
• Date: Wed, 15 Jan 2025 21:36:19 GMT
• Title: Embodied Scene Understanding for Vision Language Models via MetaVQA
• Authors: Weizhen Wang, Chenda Duan, Zhenghao Peng, Yuxin Liu, Bolei Zhou,
• Abstract summary: Vision Language Models (VLMs) demonstrate significant potential as embodied AI agents for various mobility applications.<n>We present MetaVQA: a comprehensive benchmark designed to assess and enhance VLMs' understanding of spatial relationships and scene dynamics.<n>Our experiments show that fine-tuning VLMs with the MetaVQA dataset significantly improves their spatial reasoning and embodied scene comprehension in safety-critical simulations.
• Score: 42.70816811661304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision Language Models (VLMs) demonstrate significant potential as embodied AI agents for various mobility applications. However, a standardized, closed-loop benchmark for evaluating their spatial reasoning and sequential decision-making capabilities is lacking. To address this, we present MetaVQA: a comprehensive benchmark designed to assess and enhance VLMs' understanding of spatial relationships and scene dynamics through Visual Question Answering (VQA) and closed-loop simulations. MetaVQA leverages Set-of-Mark prompting and top-down view ground-truth annotations from nuScenes and Waymo datasets to automatically generate extensive question-answer pairs based on diverse real-world traffic scenarios, ensuring object-centric and context-rich instructions. Our experiments show that fine-tuning VLMs with the MetaVQA dataset significantly improves their spatial reasoning and embodied scene comprehension in safety-critical simulations, evident not only in improved VQA accuracies but also in emerging safety-aware driving maneuvers. In addition, the learning demonstrates strong transferability from simulation to real-world observation. Code and data will be publicly available at https://metadriverse.github.io/metavqa .

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