no code implementations • 24 Nov 2016 • Naila Murray, Hervé Jégou, Florent Perronnin, Andrew Zisserman
The second one involves equalising the match of a single descriptor to the aggregated vector.
9 code implementations • 7 Sep 2016 • Matthijs Douze, Hervé Jégou, Florent Perronnin
This paper considers the problem of approximate nearest neighbor search in the compressed domain.
no code implementations • 1 Mar 2016 • Mattis Paulin, Julien Mairal, Matthijs Douze, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid
Convolutional neural networks (CNNs) have recently received a lot of attention due to their ability to model local stationary structures in natural images in a multi-scale fashion, when learning all model parameters with supervision.
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.
no code implementations • Conference 2015 • Florent Perronnin, Diane Larlus
Fisher Vectors (FV) and Convolutional Neural Networks(CNN) are two image classification pipelines with different strengths.
no code implementations • 23 Jul 2015 • Albert Gordo, Adrien Gaidon, Florent Perronnin
Convolutional Networks (ConvNets) have recently improved image recognition performance thanks to end-to-end learning of deep feed-forward models from raw pixels.
no code implementations • 18 Apr 2015 • David Novotný, Diane Larlus, Florent Perronnin, Andrea Vedaldi
Fisher Vectors and related orderless visual statistics have demonstrated excellent performance in object detection, sometimes superior to established approaches such as the Deformable Part Models.
2 code implementations • 30 Mar 2015 • Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid
Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce.
Ranked #7 on Multi-label zero-shot learning on Open Images V4
no code implementations • 16 Dec 2014 • Luca Marchesotti, Naila Murray, Florent Perronnin
We then describe how these three key components of AVA - images, scores, and comments - can be effectively leveraged to learn visual attributes.
no code implementations • 19 Aug 2014 • Yangmuzi Zhang, Diane Larlus, Florent Perronnin
A natural approach to teaching a visual concept, e. g. a bird species, is to show relevant images.
no code implementations • CVPR 2014 • Naila Murray, Florent Perronnin
Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics, such as the bag-of-visual-words (BOV) and its soft- and sparse-coding extensions.
no code implementations • CVPR 2014 • Mattis Paulin, Jerome Revaud, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid
We propose a principled algorithm Image Transformation Pursuit (ITP) for the automatic selection of a compact set of transformations.
no code implementations • CVPR 2013 • Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid
The label embedding framework offers other advantages such as the ability to leverage alternative sources of information in addition to attributes (e. g. class hierarchies) or to transition smoothly from zero-shot learning to learning with large quantities of data.