Training-free Adapter for Multi-Modal Image Matching for All-Day Visual Place Recognition

Abstract

Visual Place Recognition (VPR) identifies an image’s location by matching a query image of an unknown location against geotagged reference images. This has been a problem of interest for the computer vision community for many years. Consequently, many successful methods with impressive performance have been proposed in the literature. However, these works are primarily for the visible spectrum, restricting recognition to RGB images. This study reveals the shortcomings of the popular VPR methods for handling multi-modal RGB-Thermal (RGB-T) retrieval. Additionally, we introduce a straightforward yet effective training-free aggregator that can be integrated with any backbone model. The proposed method, motivated by the rich structures in the images, attempts to capture the correlation between them. We call it Self-Neighbourhood Support Maps (SNSM). Simple to comprehend and implement, experiments on various RGB-T datasets reveal that SNSM performs better than widely used unsupervised techniques such as VLAD by a considerable margin. The source code at: https://github.com/lfovia/SNSM.