Image Segmentation
1494 papers with code • 3 benchmarks • 20 datasets
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.
Benchmarks
These leaderboards are used to track progress in Image Segmentation
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Libraries
Use these libraries to find Image Segmentation models and implementationsDatasets
SA-1B
MCubeS
Pascal Panoptic Parts
MARIDA
EntitySeg
HuTu 80
Raw-Microscopy and Raw-Drone
Pothole Mix
TomoSAM
Most implemented papers
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs).
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
This is due to the very invariance properties that make DCNNs good for high level tasks.
Segment Anything
facebookresearch/segment-anything
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ICCV 2023
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation.
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
rwightman/pytorch-image-models
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Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation.
PointRend: Image Segmentation as Rendering
facebookresearch/detectron2
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CVPR 2020
We present a new method for efficient high-quality image segmentation of objects and scenes.
TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
Pixel-wise image segmentation is demanding task in computer vision.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
LeeJunHyun/Image_Segmentation
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In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
tensorflow/models
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CVPR 2019
Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space.
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
