Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …

In the early days, content-based image retrieval (CBIR) was studied with global features.
Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively …

Abstract Visual Place Recognition (VPR) is a crucial part of mobile robotics and autonomous
driving as well as other computer vision tasks. It refers to the process of identifying a place …

Image descriptors based on activations of Convolutional Neural Networks (CNNs) have
become dominant in image retrieval due to their discriminative power, compactness of …

Visual place recognition is a challenging task for applications such as autonomous driving
navigation and mobile robot localization. Distracting elements presenting in complex scenes …

The ability to perform pixel-wise semantic segmentation in real-time is of paramount
importance in mobile applications. Recent deep neural networks aimed at this task have the …

Optimising a ranking-based metric, such as Average Precision (AP), is notoriously
challenging due to the fact that it is non-differentiable, and hence cannot be optimised …

In recent years visual place recognition (VPR), ie, the problem of recognizing the location of
images, has received considerable attention from multiple research communities, spanning …

In this paper we address issues with image retrieval benchmarking on standard and popular
Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset, and …

We propose a novel approach for instance-level image retrieval. It produces a global and
compact fixed-length representation for each image by aggregating many region-wise …