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

Institute of Neuro- and Bioinformatics

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.

Original language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Publisher	IEEE
Publication date	09.02.2017
Article number	7850113
ISBN (Print)	978-1-5090-4241-8
ISBN (Electronic)	978-1-5090-4240-1
DOIs	https://doi.org/10.1109/SSCI.2016.7850113
Publication status	Published - 09.02.2017
Event	2016 IEEE Symposium Series on Computational Intelligence - Athens, Greece Duration: 06.12.2016 → 09.12.2016 Conference number: 126460

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10.1109/SSCI.2016.7850113

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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",

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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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Image classification with recurrent attention models

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