Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory
Table 3: Comparison with prior s-o-t-a methods. The numbers of RConcat is taken from the paper chen2018touchdown.
['Methods', 'SPL↑', 'Navigation Error↓', 'Total Steps↓']
We also evaluate our method on the TouchDown dataset chen2018touchdown. The Touchdown dataset has navigational instructions quite similar to the TalkNav dataset in terms of text length and the navigation environment. However, the Touchdown dataset has no correspondence between sub-descriptions and visual landmarks, and between sub-descriptions and sub-routes. Hence, we cannot train our method on the Touchdown dataset. We train our model on the Talk2Nav dataset and then evaluate it on the Touchdown dataset. We keep the model setting and the approach the same as used for our Talk2Nav dataset. We compare the performance of our method with Student-Forcing anderson2018vision, RPA look:before:leap:eccv18 and RConcat chen2018touchdown. We observe that our approach also outperforms other top-performing VLN methods on the TouchDown dataset chen2018touchdown.