mlsanityData to 7 Oct 2026

Kefa: A Knowledge Enhanced and Fine-grained Aligned Speaker for Navigation Instruction Generation

arXiv 2307.13368 ↗First posted 25 Jul 2023cs.CVcited by 11

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Abstract (arXiv)

We introduce a novel speaker model textscKefa for navigation instruction generation. The existing speaker models in Vision-and-Language Navigation suffer from the large domain gap of vision features between different environments and insufficient temporal grounding capability. To address the challenges, we propose a Knowledge Refinement Module to enhance the feature representation with external knowledge facts, and an Adaptive Temporal Alignment method to enforce fine-grained alignment between the generated instructions and the observation sequences. Moreover, we propose a new metric SPICE-D for navigation instruction evaluation, which is aware of the correctness of direction phrases. The experimental results on R2R and UrbanWalk datasets show that the proposed KEFA speaker achieves state-of-the-art instruction generation performance for both indoor and outdoor scenes.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 40 of its 70 references that are in the map. First posted on arXiv 25 Jul 2023.

Builds on

40 of 70 references.

References · 40

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28 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 3.

11 papers.