The OpenEQA dataset is a significant contribution in the field of Embodied Question Answering (EQA). Let me provide you with some details:
- Definition:
- Embodied Question Answering (EQA) involves understanding an environment well enough to answer questions about it in natural language.
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EQA agents can achieve this understanding through either episodic memory (as seen in agents using smart glasses) or active exploration of the environment (as in the case of mobile robots).
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OpenEQA Dataset:
- OpenEQA is the first open-vocabulary benchmark dataset for EQA that supports both episodic memory and active exploration use cases.
- It contains over 1600 high-quality human-generated questions drawn from more than 180 real-world environments.
- The dataset consists of question-answer pairs ($Q, A^$) and corresponding episode histories* ($H$).
- You can find the question-answer pairs in the file
data/open-eqa-v0.json
.
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To access the episode histories, follow the instructions provided here.
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Evaluation Protocol:
- OpenEQA also provides an automatic evaluation protocol powered by language model-based evaluation (LLM).
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This evaluation protocol correlates well with human judgment.
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Foundation Models Evaluation:
- Researchers evaluated several state-of-the-art foundation models, including GPT-4V, using the OpenEQA dataset.
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The findings revealed that these models significantly lag behind human-level performance in EQA tasks.
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Significance:
- OpenEQA serves as a straightforward, measurable, and practically relevant benchmark for current-generation foundation models.
- It poses a considerable challenge and inspires research at the intersection of Embodied AI, conversational agents, and world models.