Summary of Is 3d Convolution with 5d Tensors Really Necessary For Video Analysis?, by Habib Hajimolahoseini et al.
Is 3D Convolution with 5D Tensors Really Necessary for Video Analysis?
by Habib Hajimolahoseini, Walid Ahmed, Austin Wen, Yang Liu
First submitted to arxiv on: 23 Jul 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 This paper proposes novel techniques for implementing 3D convolutional blocks using 2D and/or 1D convolutions with 4D and/or 3D tensors. The motivation is to address the computational expense of 3D convolutions with 5D tensors, which may not be supported by edge devices in real-time applications like robots. Existing approaches mitigate this issue by splitting 3D kernels into spatial and temporal domains, but still use 3D convolutions with 5D tensors. The proposed implementation methods show significant improvements in efficiency and accuracy. Experimental results confirm that the proposed spatio-temporal processing structure outperforms the original model in terms of speed and accuracy using only 4D tensors with fewer parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make computers do better at recognizing patterns in videos without needing too much power or memory. Right now, this is hard because computers need to process lots of information at once. The researchers looked at how other people have tried to solve this problem and came up with new ideas that work even better. They tested these ideas on some examples and found that they really do make computers faster and more accurate. |
