Summary of Adaptive Focus For Efficient Video Recognition, by Yulin Wang et al.
Adaptive Focus for Efficient Video Recognition
by Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, Gao Huang
First submitted to arxiv on: 7 May 2021
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate ways to improve the efficiency of video recognition systems. They discover that a small, shifting image patch in each frame contains most of the valuable information for recognition tasks. To take advantage of this spatial redundancy, they propose a reinforcement learning-based approach called AdaFocus. This method uses a lightweight ConvNet to quickly process the full video sequence and then applies a recurrent policy network to localize the most informative regions. A high-capacity network is used for final prediction. The authors demonstrate that this approach can be efficient on modern GPU devices and can be further extended by considering temporal redundancy. They test their method on five benchmark datasets, outperforming competitive baselines in terms of efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Videos are getting longer and more complex, making it hard for computers to recognize what’s happening. Scientists found that a small part of each frame is really important for recognition, but it moves around between frames. To make video recognition faster and more efficient, they created an approach called AdaFocus. It uses two networks: one to quickly look at the whole video and another to find the most important parts. These parts are then used to make a final prediction. This method is good because it can use many computers at once, making it fast and efficient. |
Keywords
* Artificial intelligence * Reinforcement learning
