Two-stream CNNs for video action recognition implemented in Keras
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Updated
Aug 9, 2019 - Python
Two-stream CNNs for video action recognition implemented in Keras
My re-implementation of two stream action recognition
Video Recognition using Mixed Convolutional Tube (MiCT) on PyTorch with a ResNet backbone
This repository contains the code implementation used in the paper Temporally Coherent Embeddings for Self-Supervised Video Representation Learning (TCE).
Transformer for Action Recognition in PyTorch
Contains additional materials for two keras.io blog posts.
PyTorch Implementation of Attention Prompt Tuning: Parameter-Efficient Adaptation of Pre-Trained Models for Action Recognition
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.
Simple Action Recognition experimentation with the UCF101 Dataset and EfficientNets.
This repository dedicated to Adaptive Deep Learning for Environment-Agnostic Human Action Recognition. This project focuses on developing a robust deep learning system tailored for accurate identification and analysis of human actions across diverse environments, with applications spanning surveillance, security, sports, and fitness.
Project work of Cognitive Computing and Artificial Intelligence course of University of Catania.
This project builds a video classification model using CNNs for spatial feature extraction and RNNs for temporal sequence modeling. Utilizing the UCF101 dataset, it covers data preprocessing, feature extraction, model training, and evaluation, providing a comprehensive approach to action recognition in videos.
Implementation of I3D in PyTorch altered for EDR experiments
Classify UCF101 videos using one frame at a time with a CNN(InceptionV3)
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