  Visual Place Recognition (VPR) refers to the process of using computer vision
to recognize the position of the current query image. Due to the significant
changes in appearance caused by season, lighting, and time spans between query
images and database images for retrieval, these differences increase the
difficulty of place recognition. Previous methods often discarded useless
features (such as sky, road, vehicles) while uncontrolled discarding features
that help improve recognition accuracy (such as buildings, trees). To preserve
these useful features, we propose a new feature aggregation method to address
this issue. Specifically, in order to obtain global and local features that
contain discriminative place information, we added some registers on top of the
original image tokens to assist in model training. After reallocating attention
weights, these registers were discarded. The experimental results show that
these registers surprisingly separate unstable features from the original image
representation and outperform state-of-the-art methods.
