Transferable Representation Learning in Vision-and-Language Navigation
Table 4: Comparison on R2R Leaderboard Test Set. Our navigation model benefits from transfer learned representations and outperforms the known SOTA on SPL. SPL and SR are reported as percentages and NE and PL in meters.
['Model', 'PL', 'NE ↓', 'SR ↑', 'SPL ↑']
Our ALTR agent significantly outperforms the SOTA at the time on SPL–the primary metric for R2R–improving it by 5% absolute measure, and it has the lowest navigation error (NE). It furthermore ties the other two best models for SR. Compared to RCM, our ALTR agent is able to learn a more efficient policy resulting in shorter trajectories to reach the goal state, as indicated by its lower path length.