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arXiv Computer Vision By Jiagan Huang, Zikun Zhou, Hongpeng Wang, Guangming Lu, Jun Yu, Wenjie Pei

Robust Vision-Language Navigation with Awareness of Instruction-Execution Progress

Read the original on arXiv Computer Vision →

The paper introduces VLN-AIEP, a Vision‑Language Navigation framework that integrates instruction‑execution progress awareness into navigation decisions. By linking the visited trajectory to the instruction, it suppresses embeddings of already executed content and matches candidate locations with pending instruction segments. The method employs a landmark‑anchored trajectory representation to encode semantic labels and geometry, enabling progress estimation without explicit supervision and achieving strong results on R2R, RxR, CVDN, and R2R‑CE benchmarks.

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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