Code release for "Convolutional Two-Stream Network Fusion for Video Action Recognition", CVPR 2016.
SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset for video action recognition, enabling the training and evaluation of models designed to classify complex activities and objects within video sequences. The framework is distinguished by its use of dual-pathway spatiotemporal sampling to capture both slow and fast motions. It supports self-supervised video learning for pre-training models on unlabeled data and employs multigrid spatiotemporal training to optimize learning across multiple spatial and temporal resolutions. The
Convolutional neural network model for video classification trained on the Kinetics dataset.
PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition"
Non-local Neural Networks for Video Classification
facebookresearch/video-nonlocal-net 的主要功能包括：Computer Vision Research, Video Analysis, Video Representation Learning。
facebookresearch/video-nonlocal-net 的开源替代品包括： kenshohara/3d-resnets-pytorch — This project is a PyTorch implementation of 3D residual networks designed for video action recognition. It provides a… irhumshafkat/r2plus1d-pytorch — PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at… deepmind/kinetics-i3d — Convolutional neural network model for video classification trained on the Kinetics dataset. facebookresearch/slowfast — SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset… feichtenhofer/twostreamfusion — Code release for "Convolutional Two-Stream Network Fusion for Video Action Recognition", CVPR 2016. yjxiong/temporal-segment-networks — Code & Models for Temporal Segment Networks (TSN) in ECCV 2016.