Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout
Table 1: Leaderboard results under different experimental setups. NL, SR, and SPL are Navigation Length, Success Rate and Success rate weighted by Path Length. The primary metric for each setup is in italics. The best results are in bold font and the second best results are underlined.
['Models', 'Test Unseen (Leader-Board) Single Run', 'Test Unseen (Leader-Board) Single Run', 'Test Unseen (Leader-Board) Single Run', 'Test Unseen (Leader-Board) Beam Search', 'Test Unseen (Leader-Board) Beam Search', 'Test Unseen (Leader-Board) Beam Search', 'Test Unseen (Leader-Board) Pre-Explore', 'Test Unseen (Leader-Board) Pre-Explore', 'Test Unseen (Leader-Board) Pre-Explore']
In this section, we compare our agent model with the models in previous works on the Vision and Language Navigation (VLN) leaderboard. The models on the leaderboard are evaluated on a private unseen test set which contains 18 new environments. For the result, our model outperforms all other models in all experimental setups.