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    This study introduces Landmark-RxR, a fine-grained dataset for Vision-and-Language Navigation (VLN). Fine-grained data significantly enhances an agent's cross-modal alignment capabilities in VLN tasks.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Robotics

    Background:

    • Vision-and-Language Navigation (VLN) requires agents to navigate 3D indoor environments using natural language instructions.
    • A key challenge in VLN is achieving precise cross-modal alignment between the agent's trajectory and the given instructions.
    • Current methods often suffer from weak supervision due to coarse-grained training data.

    Purpose of the Study:

    • To address the challenge of weak cross-modal alignment supervision in VLN.
    • To introduce a novel, human-annotated, fine-grained dataset for VLN.
    • To explore and optimize training components using fine-grained data for improved VLN performance.

    Main Methods:

    • Introduced Landmark-RxR, a fine-grained VLN dataset with human annotations for precise supervision.
    • Investigated and adapted core training components: data augmentation, training paradigm, reward shaping, and navigation loss design.
    • Developed a novel evaluation mechanism tailored for fine-grained VLN data.

    Main Results:

    • Demonstrated that fine-grained data from Landmark-RxR effectively improves an agent's cross-modal alignment ability.
    • Showcased the advantages of fine-grained supervision in enhancing VLN task performance.
    • Validated the effectiveness of the proposed training strategies and evaluation mechanism.

    Conclusions:

    • Fine-grained data is crucial for enhancing cross-modal alignment in Vision-and-Language Navigation.
    • The Landmark-RxR dataset provides valuable fine-grained supervision for advancing VLN research.
    • Optimized training strategies leveraging fine-grained data lead to significant improvements in agent navigation capabilities.