Real-Time Semantic Segmentation
87 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
Semantic Flow for Fast and Accurate Scene Parsing
donnyyou/torchcv
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ECCV 2020
A common practice to improve the performance is to attain high resolution feature maps with strong semantic representation.
Rethinking BiSeNet For Real-time Semantic Segmentation
MichaelFan01/STDC-Seg
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CVPR 2021
BiSeNet has been proved to be a popular two-stream network for real-time segmentation.
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
XuJiacong/PIDNet
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CVPR 2023
To alleviate this problem, we propose a novel three-branch network architecture: PIDNet, which contains three branches to parse detailed, context and boundary information, respectively, and employs boundary attention to guide the fusion of detailed and context branches.
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
Therefore, additional processing steps have to be performed in order to obtain pixel-accurate segmentation masks at the full image resolution.
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
DrSleep/multi-task-refinenet
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Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards.
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots.
Efficient ConvNet for Real-time Semantic Segmentation
Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way.
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
ywangeq/PointSeg
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We take the spherical image, which is transformed from the 3D LiDAR point clouds, as input of the convolutional neural networks (CNNs) to predict the point-wise semantic map.
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation
As a pixel-level prediction task, semantic segmentation needs large computational cost with enormous parameters to obtain high performance.
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices.
