Semantic Segmentation

3013 papers with code • 78 benchmarks • 221 datasets

Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category. 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 )

Most implemented papers

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.

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.

Mask R-CNN

matterport/Mask_RCNN ICCV 2017

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

MMDetection: Open MMLab Detection Toolbox and Benchmark

open-mmlab/mmdetection 17 Jun 2019

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

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.

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.

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.

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.

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.