Visual Place Recognition~(VPR) is a task of estimating the location of a query image, predominantly executed through image retrieval using learned global descriptors from a reference database of geo-tagged images. While recent approaches have aimed to improve the scalability of VPR training by leveraging classification loss as a proxy task, this objective discretizes the feature space into distinct class regions, often overlooking visual differences between classes. This discretization makes VPR systems particularly vulnerable to extreme visual changes such as lifelong variations. To remedy these problems, we propose a novel Class-Relational Objective~(CRO) that transforms one-hot labels into soft labels by considering visual information of inter-class relations. We further enhance this method by dynamically adjusting the influence of CRO based on class weight magnitudes, which serve as an indicator of class stability as supported by derivative analysis. Comprehensive experiments across diverse benchmarks, ranging from general visual variations to extreme lifelong changes, demonstrate that our method outperforms existing methods.
Class-Relational Approach for Visual Place Recognition under Extreme Appearance Changes
IEEE Robotics and Automation Letters (RA-L), 2026