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 …

The explosive increase and ubiquitous accessibility of visual data on the Web have led to
the prosperity of research activity in image search or retrieval. With the ignorance of visual …

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect
to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a …

We introduce a loss for metric learning, which is inspired by the Lowe's matching criterion for
SIFT. We show that the proposed loss, that maximizes the distance between the closest …

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 …

This paper contributes a new high quality dataset for person re-identification, named" Market-
1501". Generally, current datasets: 1) are limited in scale; 2) consist of hand-drawn bboxes …

Abstract We design a Kernelized Few-shot Object Detector by leveraging kernelized
matrices computed over multiple proposal regions, which yield expressive non-linear …

The latest generation of Convolutional Neural Networks (CNN) have achieved impressive
results in challenging benchmarks on image recognition and object detection, significantly …

View-based methods have achieved considerable success in $3 $ D object recognition
tasks. Different from existing view-based methods pooling the view-wise features, we tackle …