Iterative Zero-Shot Localization via Semantic-Assisted Location Network

This paper considers zero-shot localization problem where the images used for localization are taken from new locations that are not included in the training dataset. We propose the Semantic-Assisted Location Network (SLN), which considers a new location essentially as a new combination of certain semantic classes. Moreover, we propose an iterative zero-shot learning method based on Expectation-Maximization (EM) algorithm to deal with the problem that the inter-class relationships of class representations in image embedding space and class embedding space are inconsistent. Experiments show that the proposed iterative zero-shot learning method outperforms start-of-the-art zero-shot localization methods by a large margin.