Visual geolocalization is a cost-effective and scalable task that involves
matching one or more query images, taken at some unknown location, to a set of
geo-tagged reference images. Existing methods, devoted to semantic features
representation, evolving towards robustness to a wide variety between query and
reference, including illumination and viewpoint changes, as well as scale and
seasonal variations. However, practical visual geolocalization approaches need
to be robust in appearance changing and extreme viewpoint variation conditions,
while providing accurate global location estimates. Therefore, inspired by
curriculum design, human learn general knowledge first and then delve into
professional expertise. We first recognize semantic scene and then measure
geometric structure. Our approach, termed CurriculumLoc, involves a delicate
design of multi-stage refinement pipeline and a novel keypoint detection and
description with global semantic awareness and local geometric verification. We
rerank candidates and solve a particular cross-domain perspective-n-point (PnP)
problem based on these keypoints and corresponding descriptors, position
refinement occurs incrementally. The extensive experimental results on our
collected dataset, TerraTrack and a benchmark dataset, ALTO, demonstrate that
our approach results in the aforementioned desirable characteristics of a
practical visual geolocalization solution. Additionally, we achieve new high
recall@1 scores of 62.6% and 94.5% on ALTO, with two different distances
metrics, respectively. Dataset, code and trained models are publicly available
on this https URL

视觉地理定位方法的多阶段课程学习以及全局和局部特征的关键点检测、描述和位置调整使其成为一种实用的视觉地理定位解决方案，取得了高召回率的好成绩。

CurriculumLoc: 通过多阶段改进增强跨领域地理定位

CurriculumLoc: Enhancing Cross-Domain Geolocalization through  Multi-Stage Refinement

In this paper we propose a novel method for image matching based on dense
local features and tailored for visual geolocalization. Dense local features
matching is robust against changes in illumination and occlusions, but not
against viewpoint shifts which are a fundamental aspect of geolocalization. Our
method, called GeoWarp, directly embeds invariance to viewpoint shifts in the
process of extracting dense features. This is achieved via a trainable module
which learns from the data an invariance that is meaningful for the task of
recognizing places. We also devise a new self-supervised loss and two new
weakly supervised losses to train this module using only unlabeled data and
weak labels. GeoWarp is implemented efficiently as a re-ranking method that can
be easily embedded into pre-existing visual geolocalization pipelines.
Experimental validation on standard geolocalization benchmarks demonstrates
that GeoWarp boosts the accuracy of state-of-the-art retrieval architectures.
The code and trained models are available at
this https URL

本文提出了一种新的基于密集局部特征的图像匹配方法，名为 GeoWarp，旨在解决视觉地理定位中的视角不变性问题，并通过新的自监督损失和两种新的弱监督损失进行训练。实验证明，GeoWarp 可以提高先进检索架构的准确性。