Towards Learning a Generic Agent forVision-and-Language Navigation via Pre-training
Table 1: Comparison with the state-of-the-art methods on R2R. Blue indicates the best value in a given setting. S indicates the single-instruction setting, M indicates the multiple-instruction setting.
['[EMPTY]', 'Agent', '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 agent Prevalent outperforms the existing models on SR and SPL by a large margin. On both validation seen and unseen environments, Prevalent outperforms other agents on nearly all metrics.