Learning and Calibrating Per-Location Classifiers for Visual Place Recognition

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How researchers see this paper

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NetVLAD: CNN Architecture for Weakly Supervised Place Recognition (2015, methodology): “We follow the standard place recognition evaluation procedure [4, 24, 64, 79, 80].”
Learned Contextual Feature Reweighting for Image Geo-Localization (2017, methodology): “Image geo-localization has been cast as an image retrieval task [2, 10, 19, 28, 66], a 2D to 3D registration task [17, 21, 32, 47, 46], and a per-location classification task [16, 62].”
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions (2018, background): “They learn features with stable appearance over time [17, 47, 49], train classifiers for place recognition [12, 28, 37, 76], and train CNNs to regress 2D-3D matches [8, 9, 60] or camera poses [19, 31, 74].”
Where am I in the dark: Exploring active transfer learning on the use of indoor localization based on thermal imaging (2016, background): “[9] changed the place recognition as a classification problem based on a classifier trained by geo-tagged images.”
Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD (2015, background): “This setting is similar to those used in [9, 12, 37].”