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

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Showing 1–10 of 199 papers

TitleStatusHype
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language AlignmentCode3
OneFormer: One Transformer to Rule Universal Image SegmentationCode3
Robust Shape Fitting for 3D Scene AbstractionCode2
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene GraphsCode2
OCNet: Object Context Network for Scene ParsingCode2
Multi-Grained Contrast for Data-Efficient Unsupervised Representation LearningCode1
EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene ParsingCode1
RT-K-Net: Revisiting K-Net for Real-Time Panoptic SegmentationCode1
DPF: Learning Dense Prediction Fields with Weak SupervisionCode1
Traffic Scene Parsing through the TSP6K DatasetCode1
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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