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Semantic Segmentation

5199 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
8,252
rwightman/pytorch-image-models
33 papers
29,758
osmr/imgclsmob
30 papers
2,917
open-mmlab/mmsegmentation
19 papers
7,405
See all 39 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

U-Net: Convolutional Networks for Biomedical Image Segmentation

labmlai/annotated_deep_learning_paper_implementations • • 18 May 2015

There is large consent that successful training of deep networks requires many thousand annotated training samples.

476
Paper
Code

Deep Residual Learning for Image Recognition

tensorflow/models • • CVPR 2016

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

469
Paper
Code

Mask R-CNN

tensorflow/models • • ICCV 2017

Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.

172
Paper
Code

MobileNetV2: Inverted Residuals and Linear Bottlenecks

tensorflow/models • • 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.

148
Paper
Code

MMDetection: Open MMLab Detection Toolbox and Benchmark

open-mmlab/mmdetection • • 17 Jun 2019

In this paper, we introduce the various features of this toolbox.

144
Paper
Code

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

google-research/vision_transformer • • ICLR 2021

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.

143
Paper
Code

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

charlesq34/pointnet • • CVPR 2017

Point cloud is an important type of geometric data structure.

109
Paper
Code

FCOS: Fully Convolutional One-Stage Object Detection

tianzhi0549/FCOS • • ICCV 2019

By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.

86
Paper
Code

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

tensorflow/models • • 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.

76
Paper
Code

Rethinking Atrous Convolution for Semantic Image Segmentation

tensorflow/models • • 17 Jun 2017

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.

75
Paper
Code
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