Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration

• URL: http://arxiv.org/abs/2203.04006v1
• Date: Tue, 8 Mar 2022 11:01:24 GMT
• Title: Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration
• Authors: Xiwen Liang, Fengda Zhu, Lingling Li, Hang Xu, Xiaodan Liang
• Abstract summary: We introduce prompt-based learning to achieve fast adaptation for language embeddings. Our model can adapt to diverse vision-language navigation tasks, including VLN and REVERIE.
• Score: 83.96729205383501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language navigation (VLN) is a challenging task due to its large searching space in the environment. To address this problem, previous works have proposed some methods of fine-tuning a large model that pretrained on large-scale datasets. However, the conventional fine-tuning methods require extra human-labeled navigation data and lack self-exploration capabilities in environments, which hinders their generalization of unseen scenes. To improve the ability of fast cross-domain adaptation, we propose Prompt-based Environmental Self-exploration (ProbES), which can self-explore the environments by sampling trajectories and automatically generates structured instructions via a large-scale cross-modal pretrained model (CLIP). Our method fully utilizes the knowledge learned from CLIP to build an in-domain dataset by self-exploration without human labeling. Unlike the conventional approach of fine-tuning, we introduce prompt-based learning to achieve fast adaptation for language embeddings, which substantially improves the learning efficiency by leveraging prior knowledge. By automatically synthesizing trajectory-instruction pairs in any environment without human supervision and efficient prompt-based learning, our model can adapt to diverse vision-language navigation tasks, including VLN and REVERIE. Both qualitative and quantitative results show that our ProbES significantly improves the generalization ability of the navigation model.

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