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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–145 of 145 papers

TitleStatusHype
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation—0
FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale Context Aggregation and Feature Space Super-resolutionCode0
Using CNNs For Users Segmentation In Video See-Through Augmented Virtuality—0
Efficient Video Semantic Segmentation with Labels Propagation and Refinement—0
FasterSeg: Searching for Faster Real-time Semantic SegmentationCode0
LiteSeg: A Novel Lightweight ConvNet for Semantic SegmentationCode0
Real-Time Semantic Segmentation via Multiply Spatial Fusion Network—0
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
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic SegmentationCode0
A Comparative Study of High-Recall Real-Time Semantic Segmentation Based on Swift Factorized NetworkCode0
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
MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based TasksCode0
DFANet: Deep Feature Aggregation for Real-Time Semantic SegmentationCode0
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
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
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 3 of 3Next →

No leaderboard results yet.