Source code of the CVPR 2021 paper: "3D CNNs with Adaptive Temporal Feature Resolutions".
Similarity Guided Sampling (SGS) is a differentiable module which can be plugged into existing 3D CNN architecture to reduce the computational cost (GFLOPs) while preserving the accuracy.
@inproceedings{sgs2021,
Author = {Mohsen Fayyaz, Emad Bahrami, Ali Diba, Mehdi Noroozi, Ehsan Adeli, Luc Van Gool, Juergen Gall},
Title = {{3D CNNs with Adaptive Temporal Feature Resolutions}},
Booktitle = {{The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) }},
Year = {2021}
}Please find installation instructions in INSTALL.md. You may follow the instructions in DATASET.md to prepare the datasets.
Follow the example in GETTING_STARTED.md.
The majority of this work is licensed under Apache 2.0 license. Portions of the project are available under separate license terms: SlowFast and 3D-ResNets-PyTorch.
The code is adapted from the following repositories: