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Real-Time Semantic Segmentation

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 )

Papers

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Showing 126–145 of 145 papers

TitleStatusHype
BiSeNet: Bilateral Segmentation Network for Real-time Semantic SegmentationCode1
Guided Upsampling Network for Real-Time Semantic Segmentation—0
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point CloudCode0
Efficient Semantic Segmentation using Gradual Grouping—0
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic SegmentationCode0
ShuffleSeg: Real-time Semantic Segmentation NetworkCode0
RTSeg: Real-time Semantic Segmentation Comparative StudyCode0
MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes—0
ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic SegmentationCode0
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNsCode0
BlitzNet: A Real-Time Deep Network for Scene UnderstandingCode0
Efficient ConvNet for Real-time Semantic SegmentationCode0
ICNet for Real-Time Semantic Segmentation on High-Resolution ImagesCode0
Pyramid Scene Parsing NetworkCode1
Full-Resolution Residual Networks for Semantic Segmentation in Street ScenesCode0
ENet: A Deep Neural Network Architecture for Real-Time Semantic SegmentationCode1
Fully Convolutional Networks for Semantic SegmentationCode0
Multi-Scale Context Aggregation by Dilated ConvolutionsCode0
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image SegmentationCode1
Conditional Random Fields as Recurrent Neural NetworksCode0
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