A keras based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".

KITTI data processing and 3D CNN for Vehicle Detection

Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.

isolated & continuous sign language recognition using CNN+LSTM/3D CNN/GCN/Encoder-Decoder

Tutorial about 3D convolutional network

A2S2K-ResNet: Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification

The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier')

A PyTorch-based library for working with 3D and 2D convolutional neural networks, with focus on semantic segmentation of volumetric biomedical image data

This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"

This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.

This repository has been integrated in https://github.com/DeepRank/deeprank2

code of DenseVoxNet and 3D-DSN

Pytorch version of the HyperDenseNet deep neural network for multi-modal image segmentation

Code for : [Pattern Recognit. Lett. 2021] "Learn to cycle: Time-consistent feature discovery for action recognition" and [IJCNN 2021] "Multi-Temporal Convolutions for Human Action Recognition in Videos".

3D HourGlass Networks for Human Pose Estimation Through Videos

[ICIP 2019] Implementation of Saliency Tubes for 3D Convolutions in Pytoch and Keras to localise the focus spatio-temporal regions of 3D CNNs.

Official source code for "Continual 3D Convolutional Neural Networks for Real-time Processing of Videos" [ECCV2022]

A pytorch based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".

HSI and LiDAR image fusion based on Deep Learning

The code is associated with the following paper "A Fast and Compact 3-D CNN for Hyperspectral Image Classification". IEEE Geoscience and Remote Sensing Letters

PyTorch implementation of the paper - Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution

codes for RS paper: Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal

Keras + pyTorch implimentation of "Deep Learning & 3D Convolutional Neural Networks for Speaker Verification"

Human action classification for video, offline and natively on iOS via Core ML

This Repo contains the updated implementation of our paper "Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131408 (16 March 2020)

[ICCVW 2019] PyTorch code for Class Visualization Pyramid for intpreting spatio-temporal class-specific activations throughout the network

Machine Learning in Neuroimaging data, Fall 2020.

DenseVoxNet in TensorFlow

Non-linear Motion Estimation for Video Frame Interpolation using Space-time Convolutions

This project is inspired by the paper "Growing Neural Cellular Automata" found at the link: https://distill.pub/2020/growing-ca/. The project's objective is to develop an extension of the methodology described in this paper to enable the generation of complex structures in three dimensions.