Memory-Efficient Active Perception for Embodied Question Answering in Dynamic Human-Populated Scenes

원문 초록

Embodied Question Answering (EQA) couples active visual perception, vision-language reasoning, and sequential decision making, and has traditionally been evaluated in temporally stable environments where visual evidence can be accumulated reliably. In realistic multimedia settings such as dynamic human-populated scenes, however, moving people and transient occlusions introduce human-induced perceptual variations: task-relevant multimodal cues become transient, viewpoint-dependent, and incompletely captured in individual views. Conventional store-then-retrieve strategies generate redundant visual evidence and incur high inference cost, making them ill-suited to such variations. This exposes two core problems for embodied agents: resolving viewpoint-dependent ambiguity, and maintaining compact evidence for efficient inference. To address these challenges, we present DIVRR (Dynamic-Informed View Refinement with Relevance-guided Rotation and adaptive memory selection), a training-free active evidence-acquisition framework for EQA. DIVRR combines question-conditioned room guidance with relevance-triggered view augmentation to guide exploration and additional observation. Selective memory admission uses the same relevance signal as view augmentation to retain high-scoring evidence, reducing stored entries while keeping the retrieved visual context compact. To evaluate EQA under human-induced perceptual variations, we introduce DynHuman-EQA, a dynamic human-populated EQA dataset constructed in Habitat, comprising dynamic and static subsets under shared scene layouts. Experiments on DynHuman-EQA and HM-EQA show that DIVRR outperforms existing baselines in both accuracy and memory efficiency. The gains are most pronounced in dynamic human-populated scenes.