Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

Related tags

Deep Learningobject-detectionsemi-supervisedcvpr2021
Overview

Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

This is the mmdetection implementation of our CVPR2021 paper:

Zhenyu Wang, Yali Li, Ye Guo, Lu Fang, Shengjin Wang. Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection. ArXiv.

This code is based on mmdetection v2.18. Please install the code according to the mmdetection step first.

Owner
GitHub Repository
Dcf-game-infrastructure-public - Contains all the components necessary to run a DC finals (attack-defense CTF) game from OOO

dcf-game-infrastructure All the components necessary to run a game of the OOO DC

Order of the Overflow 46 Sep 13, 2022
Set of models for classifcation of 3D volumes

Classification models 3D Zoo - Keras and TF.Keras This repository contains 3D variants of popular CNN models for classification like ResNets, DenseNet

69 Dec 28, 2022
DETReg: Unsupervised Pretraining with Region Priors for Object Detection

DETReg: Unsupervised Pretraining with Region Priors for Object Detection Amir Bar, Xin Wang, Vadim Kantorov, Colorado J Reed, Roei Herzig, Gal Chechik

Amir Bar 283 Dec 27, 2022
PyTorch implementation of residual gated graph ConvNets, ICLR’18

Residual Gated Graph ConvNets April 24, 2018 Xavier Bresson http://www.ntu.edu.sg/home/xbresson https://github.com/xbresson https://twitter.com/xbress

Xavier Bresson 112 Aug 10, 2022
PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"

SLAPS-GNN This repo contains the implementation of the model proposed in SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks

60 Dec 22, 2022
PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper.

deep-linear-shapes PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper. If you find this code useful i

Romain Loiseau 27 Sep 24, 2022
Learning nonlinear operators via DeepONet

DeepONet: Learning nonlinear operators The source code for the paper Learning nonlinear operators via DeepONet based on the universal approximation th

Lu Lu 239 Jan 02, 2023
Half Instance Normalization Network for Image Restoration

HINet Half Instance Normalization Network for Image Restoration, based on https://github.com/megvii-model/HINet. Dependencies NumPy PyTorch, preferabl

Holy Wu 4 Jun 06, 2022
PyTorch reimplementation of minimal-hand (CVPR2020)

Minimal Hand Pytorch Unofficial PyTorch reimplementation of minimal-hand (CVPR2020). you can also find in youtube or bilibili bare hand youtube or bil

Hao Meng 228 Dec 29, 2022
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

LSTMs for Human Activity Recognition Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amon

Guillaume Chevalier 3.1k Dec 30, 2022
[IEEE TPAMI21] MobileSal: Extremely Efficient RGB-D Salient Object Detection [PyTorch & Jittor]

MobileSal IEEE TPAMI 2021: MobileSal: Extremely Efficient RGB-D Salient Object Detection This repository contains full training & testing code, and pr

Yu-Huan Wu 52 Jan 06, 2023
[NeurIPS '21] Adversarial Attacks on Graph Classification via Bayesian Optimisation (GRABNEL)

Adversarial Attacks on Graph Classification via Bayesian Optimisation @ NeurIPS 2021 This repository contains the official implementation of GRABNEL,

Xingchen Wan 12 Dec 23, 2022
Unofficial Implementation of RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019) This repository contains python (3.5.2) implementation of

Doyup Lee 222 Dec 21, 2022
True Few-Shot Learning with Language Models

This codebase supports using language models (LMs) for true few-shot learning: learning to perform a task using a limited number of examples from a single task distribution.

Ethan Perez 124 Jan 04, 2023
Repo for our ICML21 paper Unsupervised Learning of Visual 3D Keypoints for Control

Unsupervised Learning of Visual 3D Keypoints for Control [Project Website] [Paper] Boyuan Chen1, Pieter Abbeel1, Deepak Pathak2 1UC Berkeley 2Carnegie

Boyuan Chen 34 Jul 22, 2022
Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation"

CoCosNet Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation" (CVPR 2020 oral). Update: 202

Lingbo Yang 38 Sep 22, 2021
Yolo Traffic Light Detection With Python

Yolo-Traffic-Light-Detection This project is based on detecting the Traffic light. Pretained data is used. This application entertained both real time

Ananta Raj Pant 2 Aug 08, 2022
Unrolled Generative Adversarial Networks

Unrolled Generative Adversarial Networks Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein arxiv:1611.02163 This repo contains an example notebo

Ben Poole 292 Dec 06, 2022
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Implementation

Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Implementation This project attempted to implement the paper Putting NeRF on a

254 Dec 27, 2022
TOOD: Task-aligned One-stage Object Detection, ICCV2021 Oral

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of

264 Jan 09, 2023