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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 51–60 of 145 papers

TitleStatusHype
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and ColonoscopyCode1
SegBlocks: Block-Based Dynamic Resolution Networks for Real-Time SegmentationCode1
DDANet: Dual Decoder Attention Network for Automatic Polyp SegmentationCode1
Dense Dual-Path Network for Real-time Semantic Segmentation—0
Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes—0
A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields—0
MCFNet: Multi-scale Covariance Feature Fusion Network for Real-time Semantic Segmentation—0
Feature Pyramid Encoding Network for Real-time Semantic Segmentation—0
MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes—0
MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation—0
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