Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation

• URL: http://arxiv.org/abs/2105.07645v1
• Date: Mon, 17 May 2021 07:18:43 GMT
• Title: Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation
• Authors: Giorgos Kordopatis-Zilos, Panagiotis Galopoulos, Symeon Papadopoulos, Ioannis Kompatsiaris
• Abstract summary: We propose a mixed classification-retrieval scheme for global-scale image geolocation. Our approach demonstrates very competitive performance on four public datasets.
• Score: 15.633461635276337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the problem of global-scale image geolocation, proposing a mixed classification-retrieval scheme. Unlike other methods that strictly tackle the problem as a classification or retrieval task, we combine the two practices in a unified solution leveraging the advantages of each approach with two different modules. The first leverages the EfficientNet architecture to assign images to a specific geographic cell in a robust way. The second introduces a new residual architecture that is trained with contrastive learning to map input images to an embedding space that minimizes the pairwise geodesic distance of same-location images. For the final location estimation, the two modules are combined with a search-within-cell scheme, where the locations of most similar images from the predicted geographic cell are aggregated based on a spatial clustering scheme. Our approach demonstrates very competitive performance on four public datasets, achieving new state-of-the-art performance in fine granularity scales, i.e., 15.0% at 1km range on Im2GPS3k.

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