Real-Time Semantic Segmentation
86 papers with code • 8 benchmarks • 12 datasets
Semantic Segmentation is a computer vision task that involves assigning a semantic label to each pixel in an image. In Real-Time Semantic Segmentation, the goal is to perform this labeling quickly and accurately in real-time, allowing for the segmentation results to be used for tasks such as object recognition, scene understanding, and autonomous navigation.
( Image credit: TorchSeg )
Benchmarks
These leaderboards are used to track progress in Real-Time Semantic Segmentation
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Libraries
Use these libraries to find Real-Time Semantic Segmentation models and implementationsDatasets
Cityscapes
NYUv2
COCO-Stuff
CamVid
Kvasir-SEG
Kvasir
Kvasir-Instrument
FLAME
KvasirCapsule-SEG
HelixNet
Most implemented papers
ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation
osmr/imgclsmob
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Transactions on Intelligent Transportation Systems (T-ITS) 2017
A comprehensive set of experiments on the publicly available Cityscapes dataset demonstrates that our system achieves an accuracy that is similar to the state of the art, while being orders of magnitude faster to compute than other architectures that achieve top precision.
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
sacmehta/EdgeNets
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CVPR 2019
Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4. 4% higher accuracy with 6x fewer FLOPs.
Multi-Scale Context Aggregation by Dilated Convolutions
fyu/dilation
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State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification.
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints.
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
ydhongHIT/DDRNet
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The proposed deep dual-resolution networks (DDRNets) are composed of two deep branches between which multiple bilateral fusions are performed.
LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
osmr/imgclsmob
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LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for realtime semantic segmentation.
Conditional Random Fields as Recurrent Neural Networks
torrvision/crfasrnn
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ICCV 2015
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding.
ShelfNet for Fast Semantic Segmentation
Compared with real-time segmentation models such as BiSeNet, our model achieves higher accuracy at comparable speed on the Cityscapes Dataset, enabling the application in speed-demanding tasks such as street-scene understanding for autonomous driving.
In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images
orsic/swiftnet
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Recent success of semantic segmentation approaches on demanding road driving datasets has spurred interest in many related application fields.
