Visual Place Recognition (VPR) aims to estimate the location of an image by
treating it as a retrieval problem. VPR uses a database of geo-tagged images
and leverages deep neural networks to extract a global representation, called
descriptor, from each image. While the training data for VPR models often
originates from diverse, geographically scattered sources (geo-tagged images),
the training process itself is typically assumed to be centralized. This
research revisits the task of VPR through the lens of Federated Learning (FL),
addressing several key challenges associated with this adaptation. VPR data
inherently lacks well-defined classes, and models are typically trained using
contrastive learning, which necessitates a data mining step on a centralized
database. Additionally, client devices in federated systems can be highly
heterogeneous in terms of their processing capabilities. The proposed FedVPR
framework not only presents a novel approach for VPR but also introduces a new,
challenging, and realistic task for FL research, paving the way to other image
retrieval tasks in FL.

通过 Federated Learning，本研究提出了一种新颖的 Visual Place Recognition (VPR) 方法，通过使用深度神经网络提取图像的全局表示，并解决了 VPR 中的一些关键挑战，为 FL 领域的其他图像检索任务铺平了道路。

通过联邦学习的协作视觉地点识别

Collaborative Visual Place Recognition through Federated Learning

Geo-tagged images are publicly available in large quantities, whereas labels
such as object classes are rather scarce and expensive to collect. Meanwhile,
contrastive learning has achieved tremendous success in various natural image
and language tasks with limited labeled data. However, existing methods fail to
fully leverage geospatial information, which can be paramount to distinguishing
objects that are visually similar. To directly leverage the abundant geospatial
information associated with images in pre-training, fine-tuning, and inference
stages, we present Contrastive Spatial Pre-Training (CSP), a self-supervised
learning framework for geo-tagged im- ages. We use a dual-encoder to separately
encode the images and their corresponding geo-locations, and use contrastive
objectives to learn effective location representations from images, which can
be transferred to downstream supervised tasks such as image classification.
Experiments show that CSP can improve model performance on both iNat2018 and
fMoW datasets. Especially, on iNat2018, CSP significantly boosts the model
performance with 10-34% relative improvement with various labeled training data
sampling ratios.

本研究提出了一种基于自监督学习的框架 Contrastive Spatial Pre-Training（CSP），用于处理大量具有地理标记信息的图像，通过对地理信息进行编码和对比学习，可以提高模型在图像分类任务中的性能。

CSP：自监督对比空间预训练用于地理空间视觉表示

CSP: Self-Supervised Contrastive Spatial Pre-Training for  Geospatial-Visual Representations

Recognizing the country where a picture has been taken has many potential
applications, such as identification of fake news and prevention of
disinformation campaigns. Previous works focused on the estimation of the
geo-coordinates where a picture has been taken. Yet, recognizing in which
country an image was taken could be more critical, from a semantic and forensic
point of view, than estimating its spatial coordinates. In the above framework,
this paper provides two contributions. First, we introduce the VIPPGeo dataset,
containing 3.8 million geo-tagged images. Secondly, we used the dataset to
train a model casting the country recognition problem as a classification
problem. The experiments show that our model provides better results than the
current state of the art. Notably, we found that asking the network to identify
the country provides better results than estimating the geo-coordinates and
then tracing them back to the country where the picture was taken.

本文利用 3.8 万张包含地理位置信息的图像构建数据集 VIPPGeo，并利用该数据集进行国家识别问题的分类模型训练与实验，发现比直接基于地理坐标估计图片拍摄地并追溯国家更为有效，结果优于现有研究。