In this paper we address the task of visual place recognition (VPR), where
the goal is to retrieve the correct GPS coordinates of a given query image
against a huge geotagged gallery. While recent works have shown that building
descriptors incorporating semantic and appearance information is beneficial,
current state-of-the-art methods opt for a top down definition of the
significant semantic content. Here we present the first VPR algorithm that
learns robust global embeddings from both visual appearance and semantic
content of the data, with the segmentation process being dynamically guided by
the recognition of places through a multi-scale attention module. Experiments
on various scenarios validate this new approach and demonstrate its performance
against state-of-the-art methods. Finally, we propose the first synthetic-world
dataset suited for both place recognition and segmentation tasks.

本研究旨在通过多尺度注意力模块，实现从视觉和语义内容中学习鲁棒全局嵌入以及动态引导的分割过程，以提高视觉地点识别的准确性，同时提出第一个适用于地点识别和分割任务的合成世界数据集，实验证明方法在不同情景下具有良好性能。