A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues
This addresses the problem of cross-modal alignment and feature-level localization for artificial agents in outdoor VLN, representing a strong specific gain rather than a foundational advancement.
The paper tackles the challenge of boosting relevant features in Vision-and-Language Navigation (VLN) by introducing a priority map module inspired by neuropsychology, which integrates with a feature-location framework to double task completion rates and achieve state-of-the-art performance on the Touchdown benchmark.
In a busy city street, a pedestrian surrounded by distractions can pick out a single sign if it is relevant to their route. Artificial agents in outdoor Vision-and-Language Navigation (VLN) are also confronted with detecting supervisory signal on environment features and location in inputs. To boost the prominence of relevant features in transformer-based architectures without costly preprocessing and pretraining, we take inspiration from priority maps - a mechanism described in neuropsychological studies. We implement a novel priority map module and pretrain on auxiliary tasks using low-sample datasets with high-level representations of routes and environment-related references to urban features. A hierarchical process of trajectory planning - with subsequent parameterised visual boost filtering on visual inputs and prediction of corresponding textual spans - addresses the core challenges of cross-modal alignment and feature-level localisation. The priority map module is integrated into a feature-location framework that doubles the task completion rates of standalone transformers and attains state-of-the-art performance on the Touchdown benchmark for VLN. Code and data are referenced in Appendix C.