We address representation learning for large-scale instance-level image retrieval. Apart
from backbone, training pipelines and loss functions, popular approaches have focused on …

How important is it for training and evaluation sets to not have class overlap in image
retrieval? We revisit Google Landmarks v2 clean the most popular training set by identifying …

Instance image retrieval could greatly benefit from discovering objects in the image dataset.
This not only helps produce more reliable feature representation but also better informs …

We propose a novel CNN architecture called ACTNET for robust instance image retrieval
from large-scale datasets. Our key innovation is a learnable activation layer designed to …

Although the unsupervised discovery and localization of common objects from within a set of
images has received considerable attention, the difficulty of this task means that current …

One of the key problems in image retrieval systems is the presence of irrelevant and noisy
image content. Such content can cause significant confusion for the system. Therefore, there …

Neural network representations proved to be relevant for many computer vision tasks such
as image classification, object detection, segmentation or instance-level image retrieval. A …

This manuscript is about a journey. The journey of computer vision and machine learning
research from the early years of Gabor filters and linear classifiers to surpassing human …