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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 101–125 of 145 papers

TitleStatusHype
RITnet: Real-time Semantic Segmentation of the Eye for Gaze TrackingCode0
Feature Pyramid Encoding Network for Real-time Semantic SegmentationCode0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
HarDNet: A Low Memory Traffic NetworkCode1
A Comparative Study of High-Recall Real-Time Semantic Segmentation Based on Swift Factorized NetworkCode0
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic SegmentationCode0
ESNet: An Efficient Symmetric Network for Real-time Semantic SegmentationCode0
DiCENet: Dimension-wise Convolutions for Efficient NetworksCode0
In Defense of Pre-Trained ImageNet Architectures for Real-Time Semantic Segmentation of Road-Driving ImagesCode0
LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic SegmentationCode0
Template-Based Automatic Search of Compact Semantic Segmentation ArchitecturesCode1
DFANet: Deep Feature Aggregation for Real-Time Semantic SegmentationCode0
MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based TasksCode0
In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving ImagesCode1
Fast-SCNN: Fast Semantic Segmentation NetworkCode0
Random Forest with Learned Representations for Semantic Segmentation—0
Design of Real-time Semantic Segmentation Decoder for Automated Driving—0
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural NetworkCode0
ShelfNet for Fast Semantic SegmentationCode0
Background Subtraction with Real-time Semantic Segmentation—0
Light-Weight RefineNet for Real-Time Semantic SegmentationCode0
Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic SegmentationCode0
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic SegmentationCode0
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric AnnotationsCode0
Efficient Road Lane Marking Detection with Deep Learning—0
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