Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09541 (cs)
[Submitted on 10 Mar 2026]
Title:Memory-Guided View Refinement for Dynamic Human-in-the-loop EQA
Authors:Xin Lu, Rui Li, Xun Huang, Weixin Li, Chuanqing Zhuang, Jiayuan Li, Zhengda Lu, Jun Xiao, Yunhong Wang
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Abstract:Embodied Question Answering (EQA) has traditionally been evaluated in temporally stable environments where visual evidence can be accumulated reliably. However, in dynamic, human-populated scenes, human activities and occlusions introduce significant perceptual non-stationarity: task-relevant cues are transient and view-dependent, while a store-then-retrieve strategy over-accumulates redundant evidence and increases inference cost. This setting exposes two practical challenges for EQA agents: resolving ambiguity caused by viewpoint-dependent occlusions, and maintaining compact yet up-to-date evidence for efficient inference. To enable systematic study of this setting, we introduce DynHiL-EQA, a human-in-the-loop EQA dataset with two subsets: a Dynamic subset featuring human activities and temporal changes, and a Static subset with temporally stable observations. To address the above challenges, we present DIVRR (Dynamic-Informed View Refinement and Relevance-guided Adaptive Memory Selection), a training-free framework that couples relevance-guided view refinement with selective memory admission. By verifying ambiguous observations before committing them and retaining only informative evidence, DIVRR improves robustness under occlusions while preserving fast inference with compact memory. Extensive experiments on DynHiL-EQA and the established HM-EQA dataset demonstrate that DIVRR consistently improves over existing baselines in both dynamic and static settings while maintaining high inference efficiency.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM) |
| Cite as: | arXiv:2603.09541 [cs.CV] |
| (or arXiv:2603.09541v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09541
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View a PDF of the paper titled Memory-Guided View Refinement for Dynamic Human-in-the-loop EQA, by Xin Lu and 8 other authors
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