Summary of Flatten: Video Action Recognition Is An Image Classification Task, by Junlin Chen et al.
Flatten: Video Action Recognition is an Image Classification task
by Junlin Chen, Chengcheng Xu, Yangfan Xu, Jian Yang, Jun Li, Zhiping Shi
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel video representation architecture, Flatten, is introduced to bridge the gap between image-understanding and video-understanding tasks. This plug-and-play module can be seamlessly integrated into any image-understanding network for efficient and effective 3D temporal data processing. By applying specific flattening operations, 3D spatiotemporal data is transformed into 2D spatial information, allowing ordinary image understanding models to capture temporal dynamic and spatial semantic information for video action recognition. Experimental results on Kinetics-400, Something-Something v2, and HMDB-51 datasets with Uniformer, SwinV2, and ResNet models demonstrate significant performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to understand videos by converting 3D data into 2D information that can be analyzed by image-understanding models. This helps make video analysis easier and more efficient. The researchers tested this method on several datasets and found it works better than other methods. |
Keywords
» Artificial intelligence » Resnet » Spatiotemporal
