Pretraining on ϕ2 for the feature-level localisation sub-
module gP M F is conducted with the component integrated
in the framework FLPM and the new MC-10 dataset. Sam-
ples in DT rain
consist of 8,100 landmarks in 10 US cities.
To demonstrate the utility of open source tools in designing
systems for outdoor VLN, the generation process leverages
free and accessible resources that enable targeted query-
ing. Entity IDs for landmarks sourced from the Wikidata
Knowledge Graph are the basis for downloading textual
summaries and images from the MediaWiki and WikiMe-
dia APIs. Additional details on MC-10 are available in
SupMat:Sec.3. The aim in generating the MC-10 dataset
is to optimise ΘgP M F such that features relating to YϕV LN
are detected in inputs XϕV LN . We opt for open A multi-
objective loss for ϕ2 consists of cross-modal matching over
the paired samples (xl, xv) - and a second objective com-
prising a prediction on the geolocation of the entity. In the
APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues