1 code implementation • 18 Oct 2021 • Yannis Kalantidis, Carlos Lassance, Jon Almazan, Diane Larlus
Dimensionality reduction methods are unsupervised approaches which learn low-dimensional spaces where some properties of the initial space, typically the notion of "neighborhood", are preserved.
2 code implementations • ICCV 2019 • Jerome Revaud, Jon Almazan, Rafael Sampaio de Rezende, Cesar Roberto de Souza
Recent deep models for image retrieval have outperformed traditional methods by leveraging ranking-tailored loss functions, but important theoretical and practical problems remain.
no code implementations • 16 Jan 2018 • Jon Almazan, Bojana Gajic, Naila Murray, Diane Larlus
In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification.
4 code implementations • 25 Oct 2016 • Albert Gordo, Jon Almazan, Jerome Revaud, Diane Larlus
Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it.
Ranked #13 on Image Retrieval on ROxford (Medium)
3 code implementations • 5 Apr 2016 • Albert Gordo, Jon Almazan, Jerome Revaud, Diane Larlus
We propose a novel approach for instance-level image retrieval.
Ranked #3 on Image Retrieval on Oxf105k
no code implementations • 3 Mar 2016 • Gabriela Csurka, Diane Larlus, Albert Gordo, Jon Almazan
In this article we study the problem of document image representation based on visual features.
no code implementations • ICCV 2015 • Albert Gordo, Jon Almazan, Naila Murray, Florent Perronnin
The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images.