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Python package built to ease deep learning on graph, on top of existing DL frameworks.
A collaboration friendly studio for NeRFs
A PyTorch implementation of the Transformer model in "Attention is All You Need".
Implementation of Graph Convolutional Networks in TensorFlow
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
Representation learning on large graphs using stochastic graph convolutions.
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
Deformable DETR: Deformable Transformers for End-to-End Object Detection.
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
OpenMMLab Self-Supervised Learning Toolbox and Benchmark
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.
Deep Hough Voting for 3D Object Detection in Point Clouds
Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
Code for "OnePose: One-Shot Object Pose Estimation without CAD Models", CVPR 2022
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
A research protocol for deep graph matching.
official implementation for the paper "Simplifying Graph Convolutional Networks"
Kernel Point Convolution implemented in PyTorch
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
This is a complete package of recent deep learning methods for 3D point clouds in pytorch (with pretrained models).
A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)
[CVPR2022] Geometric Transformer for Fast and Robust Point Cloud Registration
Jittor implementation of PCT:Point Cloud Transformer