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Category Archives: Segmentation

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation

The implementation of Reducing Infromation Bottleneck for Weakly Supervised Semantic Segmentation, Jungbeom Lee, Jooyoung Choi, Jisoo Mok, and Sungroh Yoon, NeurIPS 2021. [[paper]] Abstract Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus

SIIM-ACR Pneumothorax Segmentation With Python

Model segmentation classification Augmentations Used following transforms from [albumentations] RESIZE_SIZE = 1024 # or 768 train_transform = albumentations.Compose([ albumentations.Resize(RESIZE_SIZE, RESIZE_SIZE), albumentations.OneOf([ albumentations.RandomGamma(gamma_limit=(60, 120), p=0.9), albumentations.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2,    

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

by Bo Dong, Wenhai Wang, Deng-Ping Fan, Jinpeng Li, Huazhu Fu, & Ling Shao. This repo is the official implementation of “Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers”. 1. Introduction Polyp-PVT is initially described in arxiv. Most polyp segmentation methods use CNNs as their backbone,

A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation

The code of: A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains, arXiv pre-print 2019 paper. Introduction We conduct the first comprehensive analysis of Weakly-Supervised Semantic Segmentation (WSSS) with image label supervision in different image domains. WSSS has been almost exclusively evaluated on

Hypercorrelation Squeeze for Few-Shot Segmentation

Hypercorrelation Squeeze for Few-Shot Segmentation This is the implementation of the paper “Hypercorrelation Squeeze for Few-Shot Segmentation” by Juhong Min, Dahyun Kang, and Minsu Cho. Implemented on Python 3.7 and Pytorch 1.5.1. For more information, check out project [website] and the paper on [arXiv]. Requirements

A semantic segmentation toolbox based on PyTorch

vedaseg vedaseg is an open source semantic segmentation toolbox based on PyTorch. Features Modular Design We decompose the semantic segmentation framework into different components. The flexible and extensible design make it easy to implement a customized semantic segmentation project by combining different modules like building