Figure 4: Submodule gP M T P estimates a step count (cnttr) on
a path trace. A trajectory plan (tp) is a Gaussian distribution
(N ormal) over the instruction and predicts a span for every step
ıt. This is concatenated with the prediction for the previous step.

Feature-level localisation Predicted spans are passed
with ψt to a submodule gP M F that is pretrained on cross-
modal matching in ϕ2 (see Figure 5). Feature-level oper-
ations commence with visual boost filtering. We opt for
a simple and efficient implementation from [3] where the
level of boosting is reduced to a single learned term (see
SupMat:Sec.2 for additional details).

Figure 5: Submodule gP M F commences feature-level operations
by boosting visual features in the perspective. The next opera-
tion (Cat) is a concatenation of the output from gV BF and the
linguistic output ı′
t from the alignment process above. A precise
prediction for the relevant span el is returned by gP rL.

η composed of e′
APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues