513 Open source projects that are alternatives of or similar to Advsemiseg

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)

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SegAN: Semantic Segmentation with Adversarial Learning

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Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge

Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge

Pytorch implementation of Virtual Adversarial Training

[CVPR 2020] Semi-Supervised Semantic Segmentation with Cross-Consistency Training.

PyTorch implementation of Adversarially Learned Inference (BiGAN).

[CVPR21] Implementation of our work "Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation"

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Reimplementing cool papers in PyTorch...

Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

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Single-Stage Semantic Segmentation from Image Labels (CVPR 2020)

DeepLabv3 built in TensorFlow

This is Pytorch re-implementation of our CVPR 2020 paper "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation" (https://arxiv.org/abs/1911.10194)

BCDU-Net : Medical Image Segmentation

Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics.

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

TensorFlow-based implementation of "Pyramid Scene Parsing Network".

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Learning to Cluster. A deep clustering strategy.

Real-Time 3D Semantic Reconstruction from 2D data

Code for https://arxiv.org/abs/1611.10080

Amazing Semantic Segmentation on Tensorflow && Keras (include FCN, UNet, SegNet, PSPNet, PAN, RefineNet, DeepLabV3, DeepLabV3+, DenseASPP, BiSegNet)

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Semantic Segmentation Architectures Implemented in PyTorch

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A list of popular deep learning models related to classification, segmentation and detection problems

Pixel-wise segmentation on VOC2012 dataset using pytorch.

🚘 Easiest Fully Convolutional Networks

Real time semantic slam in ROS with a hand held RGB-D camera

Reimplementation of DeepLabV3

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

SemanticKITTI API for visualizing dataset, processing data, and evaluating results.

Winning solution for the Kaggle TGS Salt Identification Challenge.

FC-DenseNet in PyTorch for Semantic Segmentation

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"

real-time network architecture for mobile devices and semantic segmentation

Lightweight models for real-time semantic segmentation(include mobilenetv1-v3, shufflenetv1-v2, igcv3, efficientnet).

[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

DeepLab-ResNet rebuilt in Pytorch

Pytorch code for semantic segmentation using ERFNet

Lip and hair color editor using face parsing maps.

Awesome Domain Adaptation Python Toolbox

Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/

Segmentation models with pretrained backbones. PyTorch.

Indoor segmentation for robot navigating, which is based on deeplab model in TensorFlow.

A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains (IJCV submission)

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

proof-of-concept program that detects rail-track with semantic segmentation for autonomous train system

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[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

This repository contains the source code of our work on designing efficient CNNs for computer vision

Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"

code for our TPAMI 2021 paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"