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Image Segmentation

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하위 태스크: 1
평가표: 13
보존된 평가 기록: 34

Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Sjyhne/ContrastiveGeoSeg

공식 코드

A Contrastive Learning Scheme with Transformer Innate Patches

https://github.com/Smart-Wheelchair-RRC/o3d-sim

공식 코드

Open-Set 3D Semantic Instance Maps for Vision Language Navigation -- O3D-SIM

https://github.com/Soumyabrata/CloudSegNet

공식 코드

CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation

https://github.com/Soumyabrata/multilabel-unet

공식 코드

Multi-label Cloud Segmentation Using a Deep Network

https://github.com/Soumyabrata/nighttime-imaging

공식 코드

Nighttime sky/cloud image segmentation

https://github.com/Spenhouet/automated-deep-photo-style-transfer

공식 코드

Automated Deep Photo Style Transfer

https://github.com/StanleyZ0528/neural-image-segmentation

공식 코드

NeuroQuantify -- An Image Analysis Software for Detection and Quantification of Neurons and Neurites using Deep Learning

https://github.com/Stefan-Yu404/MP-UNet

공식 코드

3D Medical Image Segmentation based on multi-scale MPU-Net

https://github.com/SteffenCzolbe/probabilistic_segmentation

공식 코드

Is segmentation uncertainty useful?

https://github.com/Suemin-Lee/MI_Unet

공식 코드

Analysis of Information Flow Through U-Nets

https://github.com/Suiiyu/MS-CMR2019

공식 코드

An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image

https://github.com/TUM-AIMED/PrivateSegmentation

공식 코드

Differentially private federated deep learning for multi-site medical image segmentation

https://github.com/TUTvision/Online-Panoptic-3D

공식 코드

Online panoptic 3D reconstruction as a Linear Assignment Problem

https://github.com/Taib/patch2image

공식 코드

From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal Images

https://github.com/ThomasZiegler/Efficient-Smoothing-of-Dilated-Convolutions

공식 코드

Efficient Smoothing of Dilated Convolutions for Image Segmentation

https://github.com/ThoroughImages/CAMEL

공식 코드

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation

https://github.com/TianBaoGe/DS-TransUNet

공식 코드

DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation

https://github.com/TillmannRheude/SegHiResGrad_CAM

공식 코드

Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation

https://github.com/TitorX/CFR-ICL-Interactive-Segmentation

공식 코드

CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

https://github.com/TobyPDE/FRRN

공식 코드

Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

https://github.com/TotalVariation/Exchanger4SITS

공식 코드

Revisiting the Encoding of Satellite Image Time Series

https://github.com/UARK-AICV/3DConvCaps

공식 코드

3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image Segmentation

https://github.com/UARK-AICV/AerialFormer

공식 코드

AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation

https://github.com/Visillect/mls-dataset

공식 코드

Multiple Light Source Dataset for Colour Research

https://github.com/VisionLearningGroup/taskcv-2017-public

공식 코드

VisDA: The Visual Domain Adaptation Challenge

https://github.com/VoyageWang/IteRPrimE

공식 코드

IteRPrimE: Zero-shot Referring Image Segmentation with Iterative Grad-CAM Refinement and Primary Word Emphasis

https://github.com/WangLibo1995/GeoSeg

공식 코드

Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

https://github.com/Wenxuan-1119/TransBTS

공식 코드

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

https://github.com/WuJunde/MrPrism

공식 코드

Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle

https://github.com/XavierCHEN34/ClickSEG

공식 코드

FocalClick: Towards Practical Interactive Image Segmentation

https://github.com/XiaoYunZhou27/ACNN

공식 코드

ACNN: a Full Resolution DCNN for Medical Image Segmentation

https://github.com/XinChenNottingham/SpatiallyConstrainedDCNN

공식 코드

A Spatially Constrained Deep Convolutional Neural Network for Nerve Fiber Segmentation in Corneal Confocal Microscopic Images using Inaccurate Annotations

https://github.com/YH0517/CLCI_Net

공식 코드

CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke

https://github.com/YYinn/SFDA-DDFP

공식 코드

DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation

https://github.com/Yaan-Wang/CRLN

공식 코드

Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image Segmentation

https://github.com/YalongLiu/Refined-Segmentation-R-CNN

공식 코드

Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.