Uğur KILIÇ

AGMS-GCN. AGMS-GCN: Attention-Guided Multi-Scale Graph Convolutional Networks for Skeletal-Based Action Recognition

ST-GCN. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition (ST-GCN)

STA-GCN. Spatial Temporal Attention Graph Convolutional Networks (STA-GCN)

SelfGCN. Python

Awesome-Human-Motion. An aggregation of human motion understanding research.

FCSA-GCN. Fine-to-Coarse Self-Attention Graph Convolutional Network for Skeleton-Based Action Recognition

DUSX-Dataset.

Generalization-of-Transformer-Networks-to-Graphs. Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.

ai_book. AI book for everyone

homemade-machine-learning. 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

pytorch-examples. A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

Attention-Augmented-Conv2d. Implementing Attention Augmented Convolutional Networks using Pytorch

st-gcn-1. Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

100-Days-Of-ML-Code. 100-Days-Of-ML-Code中文版

MS-G3D. [CVPR 2020 Oral] PyTorch implementation of "Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition"

MPRGDeepLearningLectureNotebook. Jupyter Notebook

deep-learning-book. Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

awesome-graph-transformer. Papers about graph transformers.

Graph-Transformer. Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)

ST-TR. Spatial Temporal Transformer Network for Skeleton-Based Activity Recognition

computer-vision-guide. :book: This guide is to help you understand the basics of the computerized image and develop computer vision projects with OpenCV. Includes Python, Java, JavaScript, C# and C++ examples.

PythonDataScienceHandbook. Python Data Science Handbook: full text in Jupyter Notebooks

Awesome-Skeleton-based-Action-Recognition_last. A curated paper list of awesome skeleton-based action recognition.

learnopencv. Learn OpenCV : C++ and Python Examples

NTU_RGBD120_Parser_python. efficiently parse the ntu rgbd 120 skeleton data with python and save it as numpy array

Shift-GCN-plus. The implementation for "Extremely Lightweight Skeleton-Based Action Recognition with ShiftGCN++." (TIP 2021).

Graphormer. Graphormer is a deep learning package that allows researchers and developers to train custom models for molecule modeling tasks. It aims to accelerate the research and application in AI for molecule science, such as material design, drug discovery, etc.

gnn_transformers_notebooks. Notebooks for the ENCCS Graph Neural Networks and Transformers workshop

deeplearningbook-discussion-questions. Repository with discussion questions for the learning machines reading group at RISE.

View-Adaptive-Neural-Networks-for-Skeleton-based-Human-Action-Recognition. View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition

NTURGB-D. Info and sample codes for "NTU RGB+D Action Recognition Dataset"

RNN-for-Human-Activity-Recognition-using-2D-Pose-Input-1. Activity Recognition from 2D pose using an LSTM RNN

Graph-Transformer-Framework. A graph transformer framework

pytorch-tutorial. PyTorch Tutorial for Deep Learning Researchers

gnn-transformers. Workshop lesson material for the GNN and Transformers workshop

Graph-Attention-Networks-GATs-. Graph Attention Networks (https://arxiv.org/abs/1710.10903)

AS-GCN. The model architecture of AS-GCN (for human action recognition)

Long-Range-Context-for-Graph-Neural-Networks-with-Global-Attention. Representing Long-Range Context for Graph Neural Networks with Global Attention

Graph_Transformer_Networks. Graph Transformer Networks (Authors' PyTorch implementation for the NeurIPS 19 paper)

LSTM-for-Human-Activity-Recognition-using-2D-Pose_Pytorch-2. Based on PyTorch library, realizing human activities recognition using 2D skeleton joint points

Graph-Convolutional-Networks. Implementation of Graph Convolutional Networks in TensorFlow

DecoupleGCN-DropGraph. The implementation for "Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition" (ECCV2020).

powerful-gnns. How Powerful are Graph Neural Networks?