We propose a soft attention based model for the task of action recognition in videos. We use multi-layered Recurrent Neural Networks (RNNs) with Long-Short Term Memory (LSTM) units which are deep both spatially and temporally. Our model learns to focus selectively on parts of the video frames and classifies videos after taking a few glimpses. The model essentially learns which parts in the frames are relevant for the task at hand and attaches higher importance to them. We evaluate the model on UCF-11 (YouTube Action), HMDB-51 and Hollywood2 datasets and analyze how the model focuses its attention depending on the scene and the action being performed.
- Python 2.7
- NumPy
- scikit learn
- skimage
- Theano
- h5py
If you use this code as part of any published research, please acknowledge the following papers:
"Action Recognition using Visual Attention."
Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov. arXiv
@article{sharma2015attention,
title={Action Recognition using Visual Attention},
author={Sharma, Shikhar and Kiros, Ryan and Salakhutdinov, Ruslan},
journal={arXiv preprint arXiv:1511.04119},
year={2015}
}
"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention."
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan
Salakhutdinov, Richard Zemel, Yoshua Bengio. To appear ICML (2015)
@article{Xu2015show,
title={Show, Attend and Tell: Neural Image Caption Generation with Visual Attention},
author={Xu, Kelvin and Ba, Jimmy and Kiros, Ryan and Cho, Kyunghyun and Courville, Aaron and Salakhutdinov, Ruslan and Zemel, Richard and Bengio, Yoshua},
journal={arXiv preprint arXiv:1502.03044},
year={2015}
}
This repsoitory is released under a revised (3-clause) BSD License. It is the implementation for our paper Action Recognition using Visual Attention. The repository uses some code from the project arctic-caption which is originally the implementation for the paper Show, Attend and Tell: Neural Image Caption Generation with Visual Attention and is also licensed under a revised (3-clause) BSD License.