Image classification with recurrent attention models

Stanislau Semeniuta; Erhardt Barth

doi:10.1109/SSCI.2016.7850113

Image classification with recurrent attention models

Stanislau Semeniuta, Erhardt Barth

Institut für Neuro- und Bioinformatik

Abstract

In this work we apply a fully differentiable Recurrent Model of Visual Attention to unconstrained real-world images. We propose a deep recurrent attention model and show that it can successfully learn to jointly localize and classify objects. We evaluate our model on multiple digit images generated from MNIST data, Google Street View images, and a fine-grained recognition dataset of 200 bird species, and show that its performance is either comparable or superior to that of alternative models.

Originalsprache	Englisch
Titel	2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Herausgeber (Verlag)	IEEE
Erscheinungsdatum	09.02.2017
Aufsatznummer	7850113
ISBN (Print)	978-1-5090-4241-8
ISBN (elektronisch)	978-1-5090-4240-1
DOIs	https://doi.org/10.1109/SSCI.2016.7850113
Publikationsstatus	Veröffentlicht - 09.02.2017
Veranstaltung	2016 IEEE Symposium Series on Computational Intelligence - Athens, Griechenland Dauer: 06.12.2016 → 09.12.2016 Konferenznummer: 126460

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title = "Image classification with recurrent attention models",

abstract = "In this work we apply a fully differentiable Recurrent Model of Visual Attention to unconstrained real-world images. We propose a deep recurrent attention model and show that it can successfully learn to jointly localize and classify objects. We evaluate our model on multiple digit images generated from MNIST data, Google Street View images, and a fine-grained recognition dataset of 200 bird species, and show that its performance is either comparable or superior to that of alternative models.",

author = "Stanislau Semeniuta and Erhardt Barth",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

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N2 - In this work we apply a fully differentiable Recurrent Model of Visual Attention to unconstrained real-world images. We propose a deep recurrent attention model and show that it can successfully learn to jointly localize and classify objects. We evaluate our model on multiple digit images generated from MNIST data, Google Street View images, and a fine-grained recognition dataset of 200 bird species, and show that its performance is either comparable or superior to that of alternative models.

AB - In this work we apply a fully differentiable Recurrent Model of Visual Attention to unconstrained real-world images. We propose a deep recurrent attention model and show that it can successfully learn to jointly localize and classify objects. We evaluate our model on multiple digit images generated from MNIST data, Google Street View images, and a fine-grained recognition dataset of 200 bird species, and show that its performance is either comparable or superior to that of alternative models.

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Y2 - 6 December 2016 through 9 December 2016

ER -

Image classification with recurrent attention models

Abstract

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