Videomae Alternatives
[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Mmaction | 1,415 | 5 years ago | 38 | apache-2.0 | Python | |||||
| An open-source toolbox for action understanding based on PyTorch | ||||||||||
| Videomae | 864 | 2 years ago | 21 | other | Python | |||||
| [NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training | ||||||||||
| Tsn Pytorch | 751 | 6 years ago | 26 | bsd-2-clause | Python | |||||
| Temporal Segment Networks (TSN) in PyTorch | ||||||||||
| Tdn | 307 | 3 years ago | apache-2.0 | Python | ||||||
| [CVPR 2021] TDN: Temporal Difference Networks for Efficient Action Recognition | ||||||||||
| Specvqgan | 262 | 2 years ago | 6 | mit | Jupyter Notebook | |||||
| Source code for "Taming Visually Guided Sound Generation" (Oral at the BMVC 2021) | ||||||||||
| Tadaconv | 177 | 2 years ago | 2 | apache-2.0 | Python | |||||
| [ICLR 2022] TAda! Temporally-Adaptive Convolutions for Video Understanding. This codebase provides solutions for video classification, video representation learning and temporal detection. | ||||||||||
| Phar | 150 | 2 years ago | apache-2.0 | Python | ||||||
| deep learning sex position classifier | ||||||||||
| Object_level_visual_reasoning | 148 | 7 years ago | 4 | Python | ||||||
| Pytorch Implementation of "Object level Visual Reasoning in Videos", F. Baradel, N. Neverova, C. Wolf, J. Mille, G. Mori , ECCV 2018 | ||||||||||
| Sstda | 122 | 4 years ago | mit | Python | ||||||
| [CVPR 2020] Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation (PyTorch) | ||||||||||
| Motionsqueeze | 92 | 4 years ago | 6 | bsd-2-clause | Python | |||||
| Official PyTorch Implementation of MotionSqueeze, ECCV 2020 | ||||||||||
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