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arXiv Computer Vision By Zixu Wang, Huaquan Zhang, Shoukai Xu, Jinwu Hu, Zhiquan Wen, Mingkui Tan

Asking Less, Adapting Better: Feedback-Efficient Test-Time Adaptation for Vision-and-Language Navigation

Read the original on arXiv Computer Vision →

The paper introduces FETA, a Feedback‑Efficient Test‑Time Adaptation method for Vision‑and‑Language Navigation. FETA uses a hierarchical sample selection strategy to identify informative trajectories and critical actions, querying only a single binary feedback per action. The method updates the policy at the queried step with a discrepancy‑aware entropy objective, achieving state‑of‑the‑art performance while reducing human interventions and maintaining robustness to noisy feedback.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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