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Vision-Language Navigation

6 papers with code ยท Computer Vision

Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments.

( Image credit: Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout )

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Cross-Lingual Vision-Language Navigation

ICLR 2020

In addition, we introduce an adversarial domain adaption loss to improve the transferring ability of our model when given a certain amount of target language data.

DOMAIN ADAPTATION VISION-LANGUAGE NAVIGATION ZERO-SHOT LEARNING

Generalized Natural Language Grounded Navigation via Environment-agnostic Multitask Learning

ICLR 2020

Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.

VISION-LANGUAGE NAVIGATION

Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks

18 Nov 2019

In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to take advantage of the additional training signals derived from the semantic information.

VISION-LANGUAGE NAVIGATION

Cross-Lingual Vision-Language Navigation

24 Oct 2019

Besides, we introduce an adversarial domain adaption loss to improve the transferring ability of our model when given a certain amount of target language data.

DOMAIN ADAPTATION VISION-LANGUAGE NAVIGATION ZERO-SHOT LEARNING

Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation

CVPR 2019

Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments.

IMITATION LEARNING VISION-LANGUAGE NAVIGATION

Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

ECCV 2018

In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices---We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task.

ROBOT NAVIGATION VISION-LANGUAGE NAVIGATION