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Semantic Segmentation

5156 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
8,216
rwightman/pytorch-image-models
33 papers
29,603
osmr/imgclsmob
30 papers
2,916
open-mmlab/mmsegmentation
19 papers
7,353
See all 39 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

PaddlePaddle/PaddleSeg • • 2 Nov 2015

We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.

74
Paper
Code

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

microsoft/Swin-Transformer • • ICCV 2021

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.

68
Paper
Code

Pyramid Scene Parsing Network

hszhao/PSPNet • • CVPR 2017

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.

67
Paper
Code

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

yanx27/Pointnet_Pointnet2_pytorch • • NeurIPS 2017

By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.

63
Paper
Code

Searching for MobileNetV3

tensorflow/models • • ICCV 2019

We achieve new state of the art results for mobile classification, detection and segmentation.

61
Paper
Code

Fully Convolutional Networks for Semantic Segmentation

pochih/fcn-pytorch • • CVPR 2015

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

53
Paper
Code

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

PaddlePaddle/PaddleSeg • • 7 Jun 2016

The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications.

49
Paper
Code

Masked Autoencoders Are Scalable Vision Learners

facebookresearch/mae • • CVPR 2022

Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels.

49
Paper
Code

YOLACT: Real-time Instance Segmentation

dbolya/yolact • • ICCV 2019

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

48
Paper
Code

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

tensorflow/models • • 2 Jun 2016

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.

47
Paper
Code
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