Generating Vision-Language Navigation Instructions Incorporated Fine-Grained Alignment Annotations

Yibo Cui; Liang Xie; Yu Zhao; Jiawei Sun; Erwei Yin

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arXiv · 2506.08566

Generating Vision-Language Navigation Instructions Incorporated Fine-Grained Alignment Annotations

Yibo Cui·Liang Xie·Yu Zhao·Jiawei Sun·Erwei Yin

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Abstract

Vision-Language Navigation (VLN) enables intelligent agents to navigate environments by integrating visual perception and natural language instructions, yet faces significant challenges due to the scarcity of fine-grained cross-modal alignment annotations. Existing datasets primarily focus on global instruction-trajectory matching, neglecting sub-instruction-level and entity-level alignments critical for accurate navigation action decision-making. To address this limitation, we propose FCA-NIG, a generative framework that automatically constructs navigation instructions with dual-level fine-grained cross-modal annotations. In this framework, an augmented trajectory is first divided into sub-trajectories, which are then processed through GLIP-based landmark detection, crafted instruction construction, OFA-Speaker based R2R-like instruction generation, and CLIP-powered entity selection, generating sub-instruction-trajectory pairs with entity-landmark annotations. Finally, these sub-pairs are aggregated to form a complete instruction-trajectory pair. The framework generates the FCA-R2R dataset, the first large-scale augmentation dataset featuring precise sub-instruction-sub-trajectory and entity-landmark alignments. Extensive experiments demonstrate that training with FCA-R2R significantly improves the performance of multiple state-of-the-art VLN agents, including SF, EnvDrop, RecBERT, and HAMT. Incorporating sub-instruction-trajectory alignment enhances agents' state awareness and decision accuracy, while entity-landmark alignment further boosts navigation performance and generalization. These results highlight the effectiveness of FCA-NIG in generating high-quality, scalable training data without manual annotation, advancing fine-grained cross-modal learning in complex navigation tasks.

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Yibo Cui, Liang Xie, Yu Zhao, Jiawei Sun, Erwei Yin. 2025-06-10. Generating Vision-Language Navigation Instructions Incorporated Fine-Grained Alignment Annotations. https://arxiv.org/abs/2506.08566

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