FAST-EQA: グローバル・ローカル領域関連性を活用した効率的な身体性質問応答
FAST-EQA: Efficient Embodied Question Answering with Global and Local Region Relevancy
質問に関連する領域を特定して探索を効率化し、視覚メモリ上のChain-of-Thought推論で高速に回答する身体性質問応答フレームワークを提案。
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著者: Haochen Zhang, Nirav Savaliya, Faizan Siddiqui, Enna Sachdeva
分類: cs.RO
原文アブストラクト
Embodied Question Answering (EQA) combines visual scene understanding, goal-directed exploration, spatial and temporal reasoning under partial observability. A central challenge is to confine physical search to question-relevant subspaces while maintaining a compact, actionable memory of observations. Furthermore, for real-world deployment, fast inference time during exploration is crucial. We introduce FAST-EQA, a question-conditioned framework that (i) identifies likely visual targets, (ii) scores global regions of interest to guide navigation, and (iii) employs Chain-of-Thought (CoT) reasoning over visual memory to answer confidently. FAST-EQA maintains a bounded scene memory that stores a fixed-capacity set of region-target hypotheses and updates them online, enabling robust handling of both single and multi-target questions without unbounded growth. To expand coverage efficiently, a global exploration policy treats narrow openings and doors as high-value frontiers, complementing local target seeking with minimal computation. Together, these components focus the agent's attention, improve scene coverage, and improve answer reliability while running substantially faster than prior approaches. On HMEQA and EXPRESS-Bench, FAST-EQA achieves state-of-the-art performance, while performing competitively on OpenEQA and MT-HM3D.
