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arXiv · 1511.04119

Action Recognition using Visual Attention

Shikhar Sharma·Ryan Kiros·Ruslan Salakhutdinov
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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.

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Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov. 2016-02-14. Action Recognition using Visual Attention. https://arxiv.org/abs/1511.04119

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