Collection of open datasets in computer vision.

A new and computationally cheap method to perform human activity recognition using PoseNet and LSTM. Where we use PoseNet for Preprocessing and LSTM for understand the sequence.

A simple but efficient transformer model for video action recognition

[Arxiv2020] The code for our paper 《Self-Supervised Temporal-Discriminative Representation Learning for Video Action Recognition》 https://arxiv.org/abs/2008.02129

A PyTorch implementation of "Spatiotemporal Multiplier Networks for Video Action Recognition"

An Online Web Game "You Perform, I Guess!" based on C3D Model

A PyTorch implementation of "SlowFast Networks for Video Recognition"

FFT PyTorch 0.3 GPU implementation & experiments with DeepFFT model in Video Action Recognition and VM Classification

Code associated with "M2A: Motion Aware Attention for Accurate Video Action Recognition"

A PyTorch implementation of RSC based on MMM 2023 paper "Weakly-supervised Temporal Action Localization with Regional Similarity Consistency"

NTU Fall 2019 Deep Learning for Computer Vision Homework 4 - Action Segmentation

Video Action Recognition using Deep Learning

This repository contains my personal code for the paper Learning Spatiotemporal Features with 3D Convolutional Networks by Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri.

Official code for CVPR'19 paper "Collaborative Spatiotemporal Feature Learning for Video Action Recognition"

3D backbone for video action recognition

SoccerAct10 is a dataset which contains 10 different soccer actions. This dataset was developed using the videos from YouTube.

This project involves the identification of different actions from video clips where the action may or may not be performed throughout the entire duration of the video. This is done using two CNN models which are 3D-CNN and LSTM models.

Single run action detector on video stream data

A PyTorch implementation of MCA based on PRICAI 2022 paper "Weakly-supervised Temporal Action Localization with Multi-head Cross-modal Attention"

This is my todo list and some useful materials