Image Segmentation
1490 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
U-Net: Convolutional Networks for Biomedical Image Segmentation
labmlai/annotated_deep_learning_paper_implementations
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
tensorflow/models
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CVPR 2018
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
tensorflow/models
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ECCV 2018
The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.
Rethinking Atrous Convolution for Semantic Image Segmentation
To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales.
Attention U-Net: Learning Where to Look for the Pancreas
ozan-oktay/Attention-Gated-Networks
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We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
Fast-SCNN: Fast Semantic Segmentation Network
The encoder-decoder framework is state-of-the-art for offline semantic image segmentation.
