Interpretable Semantic Photo Geolocalization
- URL: http://arxiv.org/abs/2104.14995v1
- Date: Fri, 30 Apr 2021 13:28:18 GMT
- Title: Interpretable Semantic Photo Geolocalization
- Authors: Jonas Theiner, Eric M\"uller-Budack, Ralph Ewerth
- Abstract summary: We present two contributions in order to improve the interpretability of a geolocalization model.
We propose a novel, semantic partitioning method which intuitively leads to an improved understanding of the predictions.
We also introduce a novel metric to assess the importance of semantic visual concepts for a certain prediction.
- Score: 4.286838964398275
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Planet-scale photo geolocalization is the complex task of estimating the
location depicted in an image solely based on its visual content. Due to the
success of convolutional neural networks (CNNs), current approaches achieve
super-human performance. However, previous work has exclusively focused on
optimizing geolocalization accuracy. Moreover, due to the black-box property of
deep learning systems, their predictions are difficult to validate for humans.
State-of-the-art methods treat the task as a classification problem, where the
choice of the classes, that is the partitioning of the world map, is the key
for success. In this paper, we present two contributions in order to improve
the interpretability of a geolocalization model: (1) We propose a novel,
semantic partitioning method which intuitively leads to an improved
understanding of the predictions, while at the same time state-of-the-art
results are achieved for geolocational accuracy on benchmark test sets; (2) We
introduce a novel metric to assess the importance of semantic visual concepts
for a certain prediction to provide additional interpretable information, which
allows for a large-scale analysis of already trained models.
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