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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 81–90 of 145 papers

TitleStatusHype
Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes—0
Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes—0
Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation—0
A-Eye: Driving with the Eyes of AI for Corner Case Generation—0
AASeg: Attention Aware Network for Real Time Semantic Segmentation—0
Real Time Egocentric Object Segmentation: THU-READ Labeling and Benchmarking Results—0
CSRNet: Cascaded Selective Resolution Network for Real-time Semantic Segmentation—0
Feature Reuse and Fusion for Real-time Semantic segmentationCode0
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data—0
MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation—0
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