We introduce AmsterTime: a challenging dataset to benchmark visual place recognition (VPR) in presence of a severe domain shift. AmsterTime offers a collection of 2,500 well-curated images matching the same scene from a street view matched to historical archival image data from Amsterdam city. The image pairs capture the same place with different cameras, viewpoints, and appearances. Unlike existing benchmark datasets, AmsterTime is directly crowdsourced in a GIS navigation platform (Mapillary). We evaluate various baselines, including non-learning, supervised and self-supervised methods, pre-trained on different relevant datasets, for both verification and retrieval tasks. Our result credits the best accuracy to the ResNet-101 model pre-trained on the Landmarks dataset for both verification and retrieval tasks by 84% and 24%, respectively. Additionally, a subset of Amsterdam landmarks is collected for feature evaluation in a classification task. Classification labels are further used to extract the visual explanations using Grad-CAM for inspection of the learned similar visuals in a deep metric learning models.

本研究介绍了一个具有挑战性的数据集AmsterTime，用于评估具有严重领域转移的视觉场所识别（VPR）。我们评估了各种基线模型，包括非学习模型、有监督模型和自监督模型，分别针对验证和检索任务，在不同相关数据集上预训练。结果表明，使用ResNet-101模型在Landmarks数据集上进行预训练的验证和检索任务的最佳准确率分别为84％和24％。此外，我们收集了一组阿姆斯特丹地标的数据集，用于进行分类任务和视觉说明的可解释性分析。

AmsterTime: 一个严重领域转移的视觉地点识别基准数据集