Use 3D ResNet to extract features of UCF101 and HMDB51 and then classify them.
-
Updated
Nov 25, 2018 - Python
Use 3D ResNet to extract features of UCF101 and HMDB51 and then classify them.
Code for NeuroImage: Clinical paper "Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network."
Video Recognition using Mixed Convolutional Tube (MiCT) on PyTorch with a ResNet backbone
A rule-based algorithm enabled the automatic extraction of disease labels from tens of thousands of radiology reports. These weak labels were used to create deep learning models to classify multiple diseases for three different organ systems in body CT.
This repository contains the source codes for the paper: "PIP: Physical Interaction Prediction via Mental Simulation with Span Selection" published at ECCV 2022.
A module for creating 3D ResNets with different depths and additional features.
Given a video-clip we retrieve k-most similar to it from a "database". We extract features from 3 modalities (audio-video-text) and create an embedding using an AutoEncoder.
Human Facial Fatigue Detection through 3D ResNet with Non-local Attention
Visualizing 3D ResNet for Medical Image Classification With Score-CAM
A PyTorch Computer Vision (CV) module library for building n-D networks flexibly ~
PyTorch Volume Models for 3D data
Add a description, image, and links to the 3d-resnet topic page so that developers can more easily learn about it.
To associate your repository with the 3d-resnet topic, visit your repo's landing page and select "manage topics."