Action Recognition using Visual Attention


Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov

Department of Computer Science
University of Toronto

Abstract

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.

BibTeX

            @inproceedings{sharma2015actrecNIPS,
  title     = {Action Recognition using Visual Attention},
  author    = {Sharma, Shikhar and Kiros, Ryan and Salakhutdinov, Ruslan},
  booktitle = {Neural Information Processing Systems (NIPS) Time Series Workshop},
  year      = {2015},
  month     = {December},
  url       = {http://shikharsharma.com/publications/pdfs/action-recognition-using-visual-attention-nips2015.pdf}
}
          

PDF URL