Towards Learning a Generic Agent forVision-and-Language Navigation via Pre-training
Table 2: Results on CVDN measured by Goal Progress. Blue indicates the best value in a given setting.
['Agent', 'Validation Unseen  [BOLD] Oracle', 'Validation Unseen  [BOLD] Navigator', 'Validation Unseen  [BOLD] Mixed', 'Test Unseen  [BOLD] Oracle', 'Test Unseen  [BOLD] Navigator', 'Test Unseen  [BOLD] Mixed']
Oracle indicates the shortest path, and Navigator indicates the path taken by the navigator. The Mixed supervision path means to take the navigator path if available, otherwise the shortest path. Note that our encoder is pre-trained on R2R dataset. We observe that it can provide significant improvement when used the new task built on the CVDN dataset. This shows that the pre-trained model can adapt well on new tasks, and yields better generalization.