Improving Vision-and-Language Navigation with Image-Text Pairs from the Web (2004.14973v2)

Abstract: Following a navigation instruction such as 'Walk down the stairs and stop at the brown sofa' requires embodied AI agents to ground scene elements referenced via language (e.g. 'stairs') to visual content in the environment (pixels corresponding to 'stairs'). We ask the following question -- can we leverage abundant 'disembodied' web-scraped vision-and-language corpora (e.g. Conceptual Captions) to learn visual groundings (what do 'stairs' look like?) that improve performance on a relatively data-starved embodied perception task (Vision-and-Language Navigation)? Specifically, we develop VLN-BERT, a visiolinguistic transformer-based model for scoring the compatibility between an instruction ('...stop at the brown sofa') and a sequence of panoramic RGB images captured by the agent. We demonstrate that pretraining VLN-BERT on image-text pairs from the web before fine-tuning on embodied path-instruction data significantly improves performance on VLN -- outperforming the prior state-of-the-art in the fully-observed setting by 4 absolute percentage points on success rate. Ablations of our pretraining curriculum show each stage to be impactful -- with their combination resulting in further positive synergistic effects.

An Overview of VLN-BERT: Enhancing Vision-and-Language Navigation Using Web-Sourced Image-Text Pairs

The paper "Improving Vision-and-Language Navigation with Image-Text Pairs from the Web" presents an innovative approach to enhancing the capabilities of embodied AI in vision-and-language navigation (VLN) tasks. The central premise of the paper is leveraging large-scale, freely available image-text datasets curated from the web, such as Conceptual Captions, to improve visual grounding in the contextually sparse domain of VLN. The researchers introduce VLN-BERT, a visiolinguistic transformer-based model designed to enhance the grounding of natural language instructions in visual perception tasks.

Key Contributions and Methodology

The paper's key contribution lies in its strategic transfer learning framework, which integrates multiple stages of pretraining to boost VLN performance notably. The training curriculum consists of three stages:

1. Language Pretraining: Initializing VLN-BERT with BERT weights trained on large language corpora for developing robust language understanding capabilities.
2. Visual Grounding: Utilizing ViLBERT weights trained on the Conceptual Captions dataset to establish strong visual-linguistic connections by aligning image regions with textual descriptions.
3. Action Grounding: Further fine-tuning the model using path-instruction pairs from the VLN dataset to better integrate action-oriented language with visual grounding.

By strategically aligning the model's pretraining on web images with the unique challenges of VLN, the authors demonstrate substantial improvements in navigation performance, quantified by superior success rates and diminished navigation errors compared to prior state-of-the-art models.

Performance and Analysis

VLN-BERT's introduction marks a significant improvement over baseline models, especially in unfamiliar settings. In quantitative evaluations against established methods such as the speaker and follower models, VLN-BERT shows enhanced path-selection capabilities, achieving higher success rates in validation unseen scenarios. A notable increase in performance arises from the full training regimen, with a considerable 9.2% improvement over pretraining devoid of web-sourced image-text data.

In ensemble configurations, which integrate VLN-BERT with traditional speaker and follower models, the proposed approach further elevates performance, achieving a 3% higher success rate on unseen validation sets than competing ensembles. Leaderboard results on the VLN test datasets consistently highlight VLN-BERT's superior ability to generalize and make informed navigation decisions.

Theoretical and Practical Implications

The paper's findings underscore the efficacy of integrating internet-scale, disembodied vision-and-language data to augment embodied AI tasks, presenting a compelling case for broader applications across similar domains requiring complex multimodal reasoning. The proposed methodology not only exploits the extensive availability of labeled web data but also mitigates the labor-intensive requirements of creating comprehensive training datasets bespoke for embodied tasks.

This approach reveals a substantial potential for enhancing other embodied AI scenarios, where common visual and textual references can be robustly pre-trained and transferred. Furthermore, the research contributes theoretically by deepening our understanding of how visiolinguistic models can bridge the domain gap between disembodied and embodied contexts.

Future Directions

Future inquiries may explore broader applications of the proposed transfer learning framework, potentially extending its use cases across diverse embodied AI tasks beyond VLN. Investigating scalable mechanism refinements that further streamline the adaptation process between static web data and dynamic embodied scenarios remains a promising avenue for research.

In conclusion, the paper effectively exemplifies the integration of large-scale internet data to advance embodied AI technologies, encouraging further exploration into utilizing abundant web resources for complex applications. Through VLN-BERT, the authors present an approach that not only enriches navigation tasks but also sets a precedence for future innovations in visiolinguistic research.