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Semantic Segmentation

5173 papers with code • 125 benchmarks • 311 datasets

Semantic Segmentation is a computer vision task in which the goal is to categorize each pixel in an image into a class or object. The goal is to produce a dense pixel-wise segmentation map of an image, where each pixel is assigned to a specific class or object. Some example benchmarks for this task are Cityscapes, PASCAL VOC and ADE20K. Models are usually evaluated with the Mean Intersection-Over-Union (Mean IoU) and Pixel Accuracy metrics.

( Image credit: CSAILVision )

Libraries

Use these libraries to find Semantic Segmentation models and implementations
PaddlePaddle/PaddleSeg
53 papers
8,228
rwightman/pytorch-image-models
33 papers
29,680
osmr/imgclsmob
30 papers
2,918
open-mmlab/mmsegmentation
19 papers
7,374
See all 39 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

A ConvNet for the 2020s

facebookresearch/ConvNeXt • • CVPR 2022

The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.

45
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Deep High-Resolution Representation Learning for Visual Recognition

open-mmlab/mmdetection • • 20 Aug 2019

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.

42
Paper
Code

Fully Convolutional Networks for Semantic Segmentation

pytorch/vision • • CVPR 2015

Convolutional networks are powerful visual models that yield hierarchies of features.

40
Paper
Code

High-Resolution Representations for Labeling Pixels and Regions

leoxiaobin/deep-high-resolution-net.pytorch • • 9 Apr 2019

The proposed approach achieves superior results to existing single-model networks on COCO object detection.

39
Paper
Code

Deformable Convolutional Networks

msracver/Deformable-ConvNets • • ICCV 2017

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules.

38
Paper
Code

Attention U-Net: Learning Where to Look for the Pancreas

ozan-oktay/Attention-Gated-Networks • • 11 Apr 2018

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.

36
Paper
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YOLACT++: Better Real-time Instance Segmentation

dbolya/yolact • • 3 Dec 2019

Then we produce instance masks by linearly combining the prototypes with the mask coefficients.

36
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Microsoft COCO: Common Objects in Context

PaddlePaddle/PaddleDetection • • 1 May 2014

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.

35
Paper
Code

ResNeSt: Split-Attention Networks

zhanghang1989/ResNeSt • • 19 Apr 2020

It is well known that featuremap attention and multi-path representation are important for visual recognition.

35
Paper
Code

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

MrGiovanni/Nested-UNet • • 18 Jul 2018

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

33
Paper
Code
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