Segmentation
4213 papers with code • 2 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find Segmentation models and implementations
CRACK500
MMFlood
Khanhha's dataset
UIIS
LLM-Seg40K
25kTrees
NPO
Most implemented papers
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
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images.
Neural Machine Translation of Rare Words with Subword Units
Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem.
Fast-SCNN: Fast Semantic Segmentation Network
The encoder-decoder framework is state-of-the-art for offline semantic image segmentation.
SOLO: Segmenting Objects by Locations
open-mmlab/mmdetection
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ECCV 2020
We present a new, embarrassingly simple approach to instance segmentation in images.
Point Transformer
Pointcept/Pointcept
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ICCV 2021
For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making.
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).
Semantic Understanding of Scenes through the ADE20K Dataset
CSAILVision/semantic-segmentation-pytorch
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Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision.
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.
