Summary of When Spatial Meets Temporal in Action Recognition, by Huilin Chen et al.
When Spatial meets Temporal in Action Recognition
by Huilin Chen, Lei Wang, Yifan Chen, Tom Gedeon, Piotr Koniusz
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 introduces a novel preprocessing technique called Temporal Integration and Motion Enhancement (TIME) layer to effectively utilize both spatial and temporal information in video action recognition. The TIME layer rearranges the original video sequence into a new frame that balances both spatial and temporal details, making it compatible with existing video models. This approach is demonstrated by integrating the TIME layer into popular action recognition models like ResNet-50, Vision Transformer, and Video Masked Autoencoders for RGB and depth video data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves video action recognition by combining spatial and temporal information. It introduces a new layer that rearranges the video sequence to balance both details. This helps existing video models recognize actions more accurately. The approach is tested on different video models and datasets, showing better results. |
Keywords
» Artificial intelligence » Resnet » Vision transformer
