MenghaoGuo

MenghaoGuo

Geek Repo

Company:Tsinghua University

Location:Beijing

Home Page:https://menghaoguo.github.io/

Twitter:@MenghaoGuo1

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MenghaoGuo's repositories

Awesome-Vision-Attentions

Summary of related papers on visual attention. Related code will be released based on Jittor gradually.

PCT

Jittor implementation of PCT:Point Cloud Transformer

AutoDeeplab

Pytorch Implementation the paper Auto-DeepLab Hierarchical Neural Architecture Search for Semantic Image Segmentation

EANet

External Attention Network

JittorGNN

Jittor implementation of Graph Convlution Networks

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PCT_Pytorch

Pytorch implementation of PCT: Point Cloud Transformer

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Enjoy-Hamburger

[ICLR 2021] Is Attention Better Than Matrix Decomposition?

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GetData

get all kinds of data by python 3

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segmentation-jittor

jittor segmentation lib

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vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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ANN

semantic segmentation,pytorch,non-local

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DANet

Dual Attention Network for Scene Segmentation (CVPR2019)

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github-slideshow

A robot powered training repository :robot:

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JSeg

JSeg is a Semantic segmentation toolbox based on MMSegmentation and Jittor

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OI-wiki

:star2: Wiki of OI / ICPC for everyone. (某大型游戏线上攻略,内含炫酷算术魔法)

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PointCloudLib

Jitor Library for Point Cloud Processing

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PointContrast

Code for paper <PointContrast: Unsupervised Pretraining for 3D Point Cloud Understanding>

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pytorch-mula

Pytorch implementation of Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation (ECCV'18)

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Segmentation.X

Papers and Benchmarks about semantic segmentation, instance segmentation, panoptic segmentation and video segmentation

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SegNeXt

Official Pytorch implementations for "SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation" (NeurIPS 2022)

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semseg

Semantic Segmentation in Pytorch

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SubdivNet

Subdivision-based Mesh Convolutional Networks

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talking-heads

Our implementation of "Few-Shot Adversarial Learning of Realistic Neural Talking Head Models" (Egor Zakharov et al.)

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