S2-BNN
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021) (by szq0214)
temporal-shift-module
[ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding (by mit-han-lab)
| S2-BNN | temporal-shift-module | |
|---|---|---|
| 1 | 3 | |
| 61 | 2,019 | |
| - | 0.4% | |
| 2.7 | 3.0 | |
| over 2 years ago | 7 months ago | |
| Python | Python | |
| - | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
S2-BNN
Posts with mentions or reviews of S2-BNN.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-26.
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[R] Knowledge Distillation, Model Ensemble and Its Application on Visual Recognition - Link to a free online lecture by the author in comments
CVPR 2021 paper ‘S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration’, Github: https://github.com/szq0214/S2-BNN
temporal-shift-module
Posts with mentions or reviews of temporal-shift-module.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-21.
- 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.
What are some alternatives?
When comparing S2-BNN and temporal-shift-module you can also consider the following projects:
MEAL-V2 - MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. In NeurIPS 2020 workshop.
mmaction2 - OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
python-socketio - Python Socket.IO server and client
react-native-sensors - A developer friendly approach for sensors in React Native
conifer - Fast inference of Boosted Decision Trees in FPGAs
conifer - Collect and revisit web pages.
gsgen - [CVPR 2024] Text-to-3D using Gaussian Splatting
generative-models - Generative Models by Stability AI
civitai - A repository of models, textual inversions, and more
instruct-pix2pix
S2-BNN vs MEAL-V2
temporal-shift-module vs mmaction2
temporal-shift-module vs python-socketio
temporal-shift-module vs react-native-sensors
temporal-shift-module vs conifer
temporal-shift-module vs conifer
temporal-shift-module vs gsgen
temporal-shift-module vs generative-models
temporal-shift-module vs civitai
temporal-shift-module vs instruct-pix2pix