We address the task of cross-domain visual place recognition, where the goal is to geolocalize a given query image against a labeled gallery, in the case where the query and the gallery belong to different visual domains. To achieve this, we focus on building a domain robust deep network by leveraging over an attention mechanism combined with few-shot unsupervised domain adaptation techniques, where we use a small number of unlabeled target domain images to learn about the target distribution. With our method, we are able to outperform the current state of the art while using two orders of magnitude less target domain images. Finally we propose a new large-scale dataset for cross-domain visual place recognition, called SVOX. The pytorch code is available at https://github.com/valeriopaolicelli/AdAGeo .

翻译：我们处理跨域视觉位置识别的任务,目标是在查询和画廊属于不同视觉域的情况下,将特定查询图像与标签的画廊进行地理定位。为了实现这一点,我们侧重于建立一个强大的领域深网络,利用一个关注机制,结合少数未受监督的域适应技术,利用少量未标记的目标域图像了解目标分布。用我们的方法,我们能够超越当前状态,同时使用两个数量级比目标域图。最后,我们提出了一个新的跨域视觉位置识别大型数据集,称为 SVOX。 Pytorch 代码可以在 https://github.com/valeriopacolicelli/AdAgeo网站上查阅。