  Indoor localization is a fundamental problem in location-based applications.
Current approaches to this problem typically rely on Radio Frequency
technology, which requires not only supporting infrastructures but human
efforts to measure and calibrate the signal. Moreover, data collection for all
locations is indispensable in existing methods, which in turn hinders their
large-scale deployment. In this paper, we propose a novel neural network based
architecture Graph Location Networks (GLN) to perform infrastructure-free,
multi-view image based indoor localization. GLN makes location predictions
based on robust location representations extracted from images through
message-passing networks. Furthermore, we introduce a novel zero-shot indoor
localization setting and tackle it by extending the proposed GLN to a dedicated
zero-shot version, which exploits a novel mechanism Map2Vec to train
location-aware embeddings and make predictions on novel unseen locations. Our
extensive experiments show that the proposed approach outperforms
state-of-the-art methods in the standard setting, and achieves promising
accuracy even in the zero-shot setting where data for half of the locations are
not available. The source code and datasets are publicly available at
https://github.com/coldmanck/zero-shot-indoor-localization-release.
