Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"

A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

Repetition code of the model for the paper "EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks" in pytorch

This is the official pytorch implementation for paper: IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration

Dynamic Graph Convolutional Neural Network for 3D point cloud semantic segmentation

支持百度竞赛数据的中文事件抽取，支持ace2005数据的英文事件抽取，本人将苏神的三元组抽取算法中的DGCNN改成了事件抽取任务，并将karas改成了本人习惯使用的pytorch，在数据加载处考虑了各种语言的扩展

Dilate Gated Convolutional Neural Network For Machine Reading Comprehension

Attention-Based Temporal Learner With Dynamical Graph Neural Network for EEG Emotion Recognition. [DEAP]&&[MEEG]

Clean & Documented TF2 implementation of "An end-to-end deep learning architecture for graph classification" (M. Zhang et al., 2018).

Code and Data for the paper "LPF-Defense: 3D Adversarial Defense based on Frequency Analysis", PLoS ONE

A pytorch implementation for an optimized DGCNN

PLEASE USE THE NEW REPO https://github.com/salehjg/DeepPoint-V2-FPGA . The deprecated in-order-queue-based repository for "DGCNN on FPGA: Acceleration of The Point CloudClassifier Using FPGAs".

Codes for the Point Cloud Analysis Project (IPL Lab, Sharif UT)

This repository contains the code to train a custom DGCNN segmentation model on 3D point cloud data and carry out post-processing to filter these point clouds from the k-regular graphs produced by the model.

Prototype of trackster tagging and smoothing for CMS HGCAL reconstructions at CERN.

This repository is a informal chainer version of the code implemented in https://github.com/WangYueFt/dgcnn.

Project for the University Master Degree Course and Thesis of Computer Vision

PyTorch implementation GCN and DGCNN paper