SOTAVerified

Scene Parsing

Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Description

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 61–70 of 199 papers

TitleStatusHype
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing—0
CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration—0
RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing—0
CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing—0
Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving ScenesCode0
Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach—0
Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing—0
Cross-CBAM: A Lightweight network for Scene Segmentation—0
Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing—0
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All datasetsPGDP5KCityscapes test

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PGDPNetTotal Accuracy84.7—Unverified
2Inter-GPSTotal Accuracy27.3—Unverified
#ModelMetricClaimedVerifiedStatus
1VCD No CoarsemIoU82.3—Unverified