Neighbor-view Enhanced Model for Vision and Language Navigation (2107.07201v3)

Abstract: Vision and Language Navigation (VLN) requires an agent to navigate to a target location by following natural language instructions. Most of existing works represent a navigation candidate by the feature of the corresponding single view where the candidate lies in. However, an instruction may mention landmarks out of the single view as references, which might lead to failures of textual-visual matching of existing methods. In this work, we propose a multi-module Neighbor-View Enhanced Model (NvEM) to adaptively incorporate visual contexts from neighbor views for better textual-visual matching. Specifically, our NvEM utilizes a subject module and a reference module to collect contexts from neighbor views. The subject module fuses neighbor views at a global level, and the reference module fuses neighbor objects at a local level. Subjects and references are adaptively determined via attention me'chanisms. Our model also includes an action module to utilize the strong orientation guidance (e.g., "turn left") in instructions. Each module predicts navigation action separately and their weighted sum is used for predicting the final action. Extensive experimental results demonstrate the effectiveness of the proposed method on the R2R and R4R benchmarks against several state-of-the-art navigators, and NvEM even beats some pre-training ones. Our code is available at https://github.com/MarSaKi/NvEM.

Neighbor-View Enhanced Model for Vision and Language Navigation: An Analytical Overview

The paper "Neighbor-view Enhanced Model for Vision and Language Navigation" introduces a novel approach to improving agent behavior in Vision and Language Navigation (VLN) tasks. This domain requires an agent to navigate through an environment using natural language instructions. The inadequacies of single-view-based navigation candidate selection have prompted this paper to extend the context by integrating visual information from neighboring views, thereby enhancing the agent's textual-visual matching capability.

The authors propose the Neighbor-View Enhanced Model (NvEM), which employs a multi-module framework to process and utilize additional visual information. NvEM is divided into three modules: subject, reference, and action modules. These components work in synergy to extract relevant visual context and contribute to the overall decision-making process of the agent.

Methodological Contributions

1. Visual Contextualization via Neighbor Views: The paper emphasizes improving the interpretability and robustness of VLN agents by expanding the visual information considered for navigation decisions. By integrating information across neighboring views, NvEM not only rectifies the limited scope of single-view approaches but also provides a richer context for visual-object recognition and route optimization.
2. Attention Mechanisms for Visual and Textual Integration: Attention mechanisms are employed across the described modules to weight visual and textual information relevant to action, subject, and reference. This ensures that each module generates action predictions based on appropriately matched visual cues in the surrounding environment.
3. Modularity and Adaptability: The modular architecture of NvEM allows for each component to focus on distinctive aspects of the navigation task: acting on orientation directives, identifying the primary navigation targets (subjects), and distinguishing landmark references. This separation improves the adaptability of the model in diverse environments.

Experimental Validation and Results

Conducted on the Room-to-Room (R2R) and Room-for-Room (R4R) tasks, experiments demonstrate NvEM’s superior performance against state-of-the-art models. Key performance metrics such as Success Rate (SR) and SPL show substantial improvements, particularly in unseen navigation environments. Of note is NvEM's ability to outperform several models employing pre-training techniques, underscoring its effective design.

The paper delineates elaborate ablation studies which clarify the contributions of individual modules to the overall performance, with results indicating the notable impact of the subject and reference modules in enriching the visual context. Further analysis confirms that incorporating neighbor views enhances navigation by providing broader visual exposure.

Implications and Future Work

The advancements introduced by NvEM hold significant theoretical and practical implications. Theoretically, the proposal of neighbor-view enhancement may inform future VLN research in modular network design and multi-view integration methodologies. Practically, it could enhance the capabilities of autonomous systems deployed in natural environments or complex indoor settings, such as robots for household tasks and automated tour systems in large facilities.

Looking forward, exploration into more intricate neighbor view integration strategies, as well as extensions into continuous navigation spaces, could further augment the effectiveness of this model. The applicability of such frameworks in adjacent domains like collaborative multi-agent systems or interactive dialogue-based navigation tasks also presents promising research avenues.

Overall, the Neighbor-view Enhanced Model offers a robust solution to longstanding challenges in VLN, paving the way for more context-aware and adaptable navigation systems in AI.