Authors
Pierre Sermanet, Andrea Frome, Esteban Real
Publication date
2014/12/22
Journal
arXiv preprint arXiv:1412.7054
Description
Abstract: This paper presents experiments extending the work of Ba et al.(2014) on recurrent
neural models for attention into less constrained visual environments, specifically fine-
grained categorization on the Stanford Dogs data set. In this work we use an RNN of the
same structure but substitute a more powerful visual network and perform large-scale pre-
training of the visual network outside of the attention RNN. Most work in attention models to
date focuses on tasks with toy or more constrained visual environments, whereas we ...
neural models for attention into less constrained visual environments, specifically fine-
grained categorization on the Stanford Dogs data set. In this work we use an RNN of the
same structure but substitute a more powerful visual network and perform large-scale pre-
training of the visual network outside of the attention RNN. Most work in attention models to
date focuses on tasks with toy or more constrained visual environments, whereas we ...
Total citations
Scholar articles
P Sermanet, A Frome, E Real - arXiv preprint arXiv:1412.7054, 2014
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