Multiple Object Recognition with Visual Attention
Abstract
We present an attention-based model for recognizing multiple objects in images. The proposed model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant regions of the input image. We show that the model learns to both localize and recognize multiple objects despite being given only class labels during training. We evaluate the model on the challenging task of transcribing house number sequences from Google Street View images and show that it is both more accurate than the state-of-the-art convolutional networks and uses fewer parameters and less computation.
Cite
Text
Ba et al. "Multiple Object Recognition with Visual Attention." International Conference on Learning Representations, 2015.Markdown
[Ba et al. "Multiple Object Recognition with Visual Attention." International Conference on Learning Representations, 2015.](https://mlanthology.org/iclr/2015/ba2015iclr-multiple/)BibTeX
@inproceedings{ba2015iclr-multiple,
title = {{Multiple Object Recognition with Visual Attention}},
author = {Ba, Jimmy and Mnih, Volodymyr and Kavukcuoglu, Koray},
booktitle = {International Conference on Learning Representations},
year = {2015},
url = {https://mlanthology.org/iclr/2015/ba2015iclr-multiple/}
}