SOTAVerified

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

Showing 126145 of 145 papers

TitleStatusHype
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 Learning0
Guided Upsampling Network for Real-Time Semantic Segmentation0
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point CloudCode0
Efficient Semantic Segmentation using Gradual Grouping0
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 scenes0
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 CNNs0
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
Full-Resolution Residual Networks for Semantic Segmentation in Street ScenesCode0
Fully Convolutional Networks for Semantic SegmentationCode0
Multi-Scale Context Aggregation by Dilated ConvolutionsCode0
Conditional Random Fields as Recurrent Neural NetworksCode0
Show:102550
← PrevPage 6 of 6Next →

No leaderboard results yet.