Auxiliary Fine-grained Alignment Constraints for Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) requires a visual agent to navigate in photo-realistic environments following instructions. Fine-grained cross-modal alignment is one critical challenge in VLN because the agent needs to focus on a particular sub-part within the complete instruction for the next movement. However, previous work failed to implement explicit supervision for matching the sub-trajectory to the corresponding sub-instruction. In this paper, we propose Auxiliary Fine-grained Alignment Constraints (AFAC) to facilitate decision-making learning during navigation. AFAC consists of two constraints, i.e., Attention Alignment Constraint (AAC) and Representation Alignment Constraint (RAC), which produce additional supervising signals from the perspective of attention and representation respectively. We test our method on the Landmark-RxR benchmark and achieve state-of-the-art results both in seen and unseen environments.