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MA-EgoQA: Question Answering over Egocentric Videos from Multiple Embodied Agents
arXiv – CS AI|Kangsan Kim, Yanlai Yang, Suji Kim, Woongyeong Yeo, Youngwan Lee, Mengye Ren, Sung Ju Hwang|
🤖AI Summary
Researchers introduce MA-EgoQA, a benchmark for evaluating AI models' ability to understand multiple egocentric video streams from embodied agents simultaneously. The benchmark includes 1.7k questions across five categories and reveals current approaches struggle with multi-agent system-level understanding.
Key Takeaways
- →MA-EgoQA benchmark addresses the novel problem of interpreting multiple long-horizon egocentric videos from embodied AI agents.
- →The dataset contains 1.7k questions spanning social interaction, task coordination, theory-of-mind, temporal reasoning, and environmental interaction.
- →Proposed baseline model EgoMAS uses shared memory across agents and agent-wise dynamic retrieval for processing multiple streams.
- →Current AI approaches show significant limitations in effectively handling multiple egocentric video streams.
- →Research highlights the need for advances in system-level understanding for future multi-agent AI collaboration.
#embodied-ai#multi-agent-systems#computer-vision#video-understanding#benchmark#egocentric-video#question-answering#ai-research
Read Original →via arXiv – CS AI
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