Summary of Uni-adafocus: Spatial-temporal Dynamic Computation For Video Recognition, by Yulin Wang et al.
Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition
by Yulin Wang, Haoji Zhang, Yang Yue, Shiji Song, Chao Deng, Junlan Feng, Gao Huang
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper presents an investigation into data redundancy in video understanding to improve computational efficiency. The authors identify spatial redundancy, where a small image patch in each frame contains most of the informative region, and formulate the patch localization problem as a dynamic decision task. They introduce AdaFocus, a spatially adaptive video recognition approach that uses a policy network to identify task-relevant regions and infer their features using a high-capacity deep network. The model can be trained end-to-end conveniently and extended by considering temporal and sample-wise redundancies. The resulting Uni-AdaFocus framework integrates dynamic computation seamlessly while preserving efficiency. Empirical experiments on seven benchmark datasets and three application scenarios show that Uni-AdaFocus outperforms competitive baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers understand videos more efficiently. It found that some parts of each frame are more important than others, so it created a system to find those parts quickly. The system uses a policy network to decide which parts to look at and then uses a deep network to figure out what they mean. This makes the computer work faster without losing accuracy. The paper also tested this system on many different videos and showed that it works better than other systems. |
