Tactical Rewind: Self-Correction via Backtrackingin Vision-and-Language Navigation
Table 1: Our results and SMNA re-implementation are shown in gray highlighted rows. Bolding indicates the best value per section and blue indicates best values overall. We include both a short and long version of our approach to compare to existing models greedy and beam search approaches.
['[EMPTY]', 'Model', 'Validation Seen TL', 'Validation Seen NE', 'Validation Seen SR', 'Validation Seen SPL', 'Validation Unseen TL', 'Validation Unseen NE', 'Validation Unseen SR', 'Validation Unseen SPL', 'Test Unseen TL', 'Test Unseen NE', 'Test Unseen SR', 'Test Unseen SPL']
Our approach significantly outperforms the existing model in terms of efficiency, matching the best overall success rate despite taking 150 - 1,000 fewer steps. This efficiency gain can be seen in the SPL metric, where our models outperform previous approaches in every setting. Note that our short trajectory model appreciably outperforms current approaches in both SR and SPL. If our agent could continue exploring, it matches existing peak success rates in half of the steps (196 vs 373).