Memory-Guided View Refinement for Dynamic Human-in-the-loop EQA
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-...
| Authors | Xin Lu, Rui Li, Xun Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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