Neighbor-view Enhanced Model for Vision and Language Navigation

Understand

Vision and Language Navigation (VLN) requires an agent to navigate to a target location by following natural language instructions.

• Most of existing works represent a navigation candidate by the feature of the corresponding single view where the candidate lies in.
• However, an instruction may mention landmarks out of the single view as references, which might lead to failures of textual-visual matching of existing methods.
• In this work, we propose a multi-module Neighbor-View Enhanced Model (NvEM) to adaptively incorporate visual contexts from neighbor views for better textual-visual matching.