论文标题
来自几个查询的自适应地理定位：一种混合方法
Adaptive-Attentive Geolocalization from few queries: a hybrid approach
论文作者
论文摘要
我们解决了跨域视觉位置识别的任务，在此目标是将给定的查询图像与标记的画廊进行地理位置化，如果查询和画廊属于不同的视觉域。为了实现这一目标，我们专注于建立一个稳健的深网，通过利用注意机制以及几乎没有射击的无监督域的适应技术，在那里我们使用少量未标记的目标域图像来了解目标分布。使用我们的方法，我们能够在使用两个数量级的目标域图像的同时胜过当前的最新状态。最后，我们提出了一个新的大规模数据集，用于跨域视觉位置识别，称为SVOX。 Pytorch代码可在https://github.com/valeriopaolicelli/adageo上获得。
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 .
