Explore the Potential Performance of Vision-and-Language Navigation Model: a Snapshot Ensemble Method
This work addresses the problem of building more robust VLN models for AI navigation tasks, but it is incremental as it builds on existing SOTA models with an ensemble technique.
The paper tackles the challenge of improving generalization in Vision-and-Language Navigation (VLN) models by proposing a snapshot-based ensemble method, which achieves new state-of-the-art performance on the R2R dataset with improved Navigation Error (NE) and Success weighted by Path Length (SPL) metrics.
Vision-and-Language Navigation (VLN) is a challenging task in the field of artificial intelligence. Although massive progress has been made in this task over the past few years attributed to breakthroughs in deep vision and language models, it remains tough to build VLN models that can generalize as well as humans. In this paper, we provide a new perspective to improve VLN models. Based on our discovery that snapshots of the same VLN model behave significantly differently even when their success rates are relatively the same, we propose a snapshot-based ensemble solution that leverages predictions among multiple snapshots. Constructed on the snapshots of the existing state-of-the-art (SOTA) model $\circlearrowright$BERT and our past-action-aware modification, our proposed ensemble achieves the new SOTA performance in the R2R dataset challenge in Navigation Error (NE) and Success weighted by Path Length (SPL).