Using two stream architecture to implement a classic action recognition method on UCF101 dataset
-
Updated
Oct 14, 2019 - Python
Using two stream architecture to implement a classic action recognition method on UCF101 dataset
Tutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101
This repository host the code for real-time action detection paper
Caffe implementation for "Hidden Two-Stream Convolutional Networks for Action Recognition"
STEP: Spatio-Temporal Progressive Learning for Video Action Detection. CVPR'19 (Oral)
Implementation Code of the paper Optical Flow Guided Feature, CVPR 2018
Video Platform for Action Recognition and Object Detection in Pytorch
Computer Vision Project : Action Recognition on UCF101 Dataset
Video Recognition using Mixed Convolutional Tube (MiCT) on PyTorch with a ResNet backbone
Use 3D ResNet to extract features of UCF101 and HMDB51 and then classify them.
My experimentation around action recognition in videos. Contains Keras implementation for C3D network based on original paper "Learning Spatiotemporal Features with 3D Convolutional Networks", Tran et al. and it includes video processing pipelines coded using mPyPl package. Model is being benchmarked on popular UCF101 dataset and achieves result…
TensorFlow implementation for "Guided Optical Flow Learning"
Action Recognition using Convolutional Neural Network (CNN)
Temporal 3D ConvNet
Pytorch inception v4 for human actions recognition.
A pytorch implementation of a text to videos GAN
Implementation of LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN
Testing code for few-shot action recognition
Add a description, image, and links to the ucf101 topic page so that developers can more easily learn about it.
To associate your repository with the ucf101 topic, visit your repo's landing page and select "manage topics."