Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training - Crossminds
CrossMind.ai logo

Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training

Sep 29, 2020
|
37 views
|
Details
Authors: Weituo Hao, Chunyuan Li, Xiujun Li, Lawrence Carin, Jianfeng Gao Description: Learning to navigate in a visual environment following natural-language instructions is a challenging task, because the multimodal inputs to the agent are highly variable, and the training data on a new task is often limited. In this paper, we present the first pre-training and fine-tuning paradigm for vision-and-language navigation (VLN) tasks. By training on a large amount of image-text-action triplets in a self-supervised learning manner, the pre-trained model provides generic representations of visual environments and language instructions. It can be easily used as a drop-in for existing VLN frameworks, leading to the proposed agent PREVALENT. It learns more effectively in new tasks and generalizes better in a previously unseen environment. The performance is validated on three VLN tasks. On the Room-to-Room benchmark, our model improves the state-of-the-art from 47\% to 51\% on success rate weighted by path length. Further, the learned representation is transferable to other VLN tasks. On two recent tasks, vision-and-dialog navigation and Help, Anna!'', the proposed PREVALENT leads to significant improvement over existing methods, achieving a new state of the art.

Comments
loading...
Reactions (0) | Note
    📝 No reactions yet
    Be the first one to share your thoughts!
loading...
Recommended