Geo-Aware Networks for Fine Grained Recognition
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Fine grained recognition distinguishes among categories with subtle visual differences. To help identify fine grained categories, other information besides images has been used. However, there has been little effort on using geolocation information to improve fine grained classification accuracy. Our contributions to this field are twofold. First, to the best of our knowledge, this is the first paper which systematically examined various ways of incorporating geolocation information to fine grained images classification - from geolocation priors, to post-processing, to feature modulation. Secondly, to overcome the situation where no fine grained dataset has complete geolocation information, we introduce, and will make public, two fine grained datasets with geolocation by providing complementary information to existing popular datasets - iNaturalist and YFCC100M. Results on these datasets show that, the best geo-aware network can achieve 8.9 iNaturalist and 5.9 results. In addition, for small image baseline models like Mobilenet V2, the best geo-aware network gives 12.6 achieving even higher performance than Inception V3 models without geolocation. Our work gives incentives to use geolocation information to improve fine grained recognition for both server and on-device models.
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