Tactical Rewind: Self-Correction via Backtracking in Vision-And-Language Navigation

We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the 2018 Room-to-Room (R2R) Vision-and-Language navigation challenge. Given a natural language instruction and photo-realistic image views of a...

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Vydané v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 6734 - 6742
Hlavní autori: Ke, Liyiming, Li, Xiujun, Bisk, Yonatan, Holtzman, Ari, Gan, Zhe, Liu, Jingjing, Gao, Jianfeng, Choi, Yejin, Srinivasa, Siddhartha
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2019
Predmet:
Backtracking
Computer vision
Decoding
Natural languages
Navigation
Pattern recognition
Photorealistic images
Robotics + Driving
Trajectory
Vision + Language
Visualization
ISSN:1063-6919
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Popis
Shrnutí:We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the 2018 Room-to-Room (R2R) Vision-and-Language navigation challenge. Given a natural language instruction and photo-realistic image views of a previously unseen environment, the agent was tasked with navigating from source to target location as quickly as possible. While all current approaches make local action decisions or score entire trajectories using beam search, ours balances local and global signals when exploring an unobserved environment. Importantly, this lets us act greedily but use global signals to backtrack when necessary. Applying FAST framework to existing state-of-the-art models achieved a 17% relative gain, an absolute 6% gain on Success rate weighted by Path Length.
ISSN:1063-6919
DOI:10.1109/CVPR.2019.00690