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 …

Image retrieval is the problem of searching an image database for items that are similar to a
query image. To address this task, two main types of image representations have been …

Retrieving object instances among cluttered scenes efficiently requires compact yet
comprehensive regional image representations. Intuitively, object semantics can help build …

Instance-level image retrieval is the task of searching in a large database for images that
match an object in a query image. To address this task, systems usually rely on a retrieval …

Instance-level image retrieval is a long-standing and challenging problem in multimedia.
Recently, fine-tuning Convolutional Neural Networks (CNNs) has become a promising …

This paper addresses the problem of very large-scale image retrieval, focusing on improving
its accuracy and robustness. We target enhanced robustness of search to factors, such as …

In asymmetric retrieval systems, models with different capacities are deployed on platforms
with different computational and storage resources. Despite the great progress, existing …

Image Retrieval is a fundamental task of obtaining images similar to the query one from a
database. A common image retrieval practice is to firstly retrieve candidate images via …

Methods that combine local and global features have recently shown excellent performance
on multiple challenging deep image retrieval benchmarks, but their use of local features …

Image retrieval systems conventionally use a two-stage paradigm, leveraging global
features for initial retrieval and local features for reranking. However, the scalability of this …