Multimodal attention networks for low-level vision-and-language navigation

• Published in: Computer vision and image understanding Vol. 210; p. 103255
• Main Authors: Landi, Federico, Baraldi, Lorenzo, Cornia, Marcella, Corsini, Massimiliano, Cucchiara, Rita
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2021
• Subjects: Embodied AI; Multi-modal attention; Vision-and-language navigation; 68T01; 68T45; Embodied AI; Vision-and-language navigation; 68T40; Multi-modal attention
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2021.103255

Vision-and-Language Navigation (VLN) is a challenging task in which an agent needs to follow a language-specified path to reach a target destination. The goal gets even harder as the actions available to the agent get simpler and move towards low-level, atomic interactions with the environment. This setting takes the name of low-level VLN. In this paper, we strive for the creation of an agent able to tackle three key issues: multi-modality, long-term dependencies, and adaptability towards different locomotive settings. To that end, we devise “Perceive, Transform, and Act” (PTA): a fully-attentive VLN architecture that leaves the recurrent approach behind and the first Transformer-like architecture incorporating three different modalities — natural language, images, and low-level actions for the agent control. In particular, we adopt an early fusion strategy to merge lingual and visual information efficiently in our encoder. We then propose to refine the decoding phase with a late fusion extension between the agent’s history of actions and the perceptual modalities. We experimentally validate our model on two datasets: PTA achieves promising results in low-level VLN on R2R and achieves good performance in the recently proposed R4R benchmark. •We devise the first Transformer-like architecture for VLN.•Good results in low-level VLN on R2R and on the recently proposed R4R.•We detail how to switch from high-level to low-level output space and vice versa.