Bayesian Relational Memory for Semantic Visual Navigation

2983335573, pp. 2769-2779, 2019.

Cited by: 14|Bibtex|Views71|DOI:https://doi.org/10.1109/ICCV.2019.00286
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Other Links: academic.microsoft.com|dblp.uni-trier.de|arxiv.org

Abstract:

We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards. BRM takes the form of a probabilistic relation graph over semantic entities (e.g., room types), which...More

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