python-socketio
temporal-shift-module
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| python-socketio | temporal-shift-module | |
|---|---|---|
| 1 | 3 | |
| 3,763 | 2,016 | |
| - | 0.6% | |
| 8.2 | 3.0 | |
| 3 days ago | 7 months ago | |
| Python | Python | |
| MIT License | MIT License |
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python-socketio
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Can’t establish a connection to the server at localhost:6595
Solution here: https://github.com/miguelgrinberg/python-socketio/issues/578
temporal-shift-module
- Stable Video Diffusion
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Can two-stream networks trained for video action recognition be used for real-time usecases?
My question mostly has to do with optical flow. One of the two-stream networks I'm interested in trying out is TSN-TSM, as there are pre-trained weights available for it on the Assembly101 dataset released a few months ago.
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I am having a hard time understanding this paper(Temporal shift module). Can some who have read it before or willing to read it explain me better in a more elaborate way?
This is the paper. (https://arxiv.org/abs/1811.08383). Here they are talking about how they can achieve temporal modelling by moving channels, which I assume are the RGB channels across frames. But I am super confused by the lingo. Here is the repo (https://github.com/mit-han-lab/temporal-shift-module). I can't give better rewards except virtual hugs. Thank you.
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