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 151–175 of 199 papers

TitleStatusHype
Weakly Supervised Class-Agnostic Motion Prediction for Autonomous Driving—0
Weakly Supervised Scene Parsing with Point-based Distance Metric Learning—0
What Synthesis is Missing: Depth Adaptation Integrated with Weak Supervision for Indoor Scene Parsing—0
1st Place Winner of the 2024 Pixel-level Video Understanding in the Wild (CVPR'24 PVUW) Challenge in Video Panoptic Segmentation and Best Long Video Consistency of Video Semantic Segmentation—0
Open Vocabulary Scene Parsing—0
ORDNet: Capturing Omni-Range Dependencies for Scene Parsing—0
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing—0
Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired—0
Predicting Scene Parsing and Motion Dynamics in the Future—0
Radar Spectra-Language Model for Automotive Scene Parsing—0
Real-Time Panoptic Segmentation from Dense Detections—0
Recurrent Convolutional Neural Networks for Scene Parsing—0
Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing—0
Re:PolyWorld - A Graph Neural Network for Polygonal Scene Parsing—0
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization—0
LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field CamerasCode0
Toward Achieving Robust Low-Level and High-Level Scene ParsingCode0
Scene Parsing via Integrated Classification Model and Variance-Based RegularizationCode0
Scene Parsing with Global Context EmbeddingCode0
Unlocking the Full Potential of Small Data with Diverse SupervisionCode0
Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving ScenesCode0
Auto-Retoucher(ART) - A framework for Background Replacement and Image EditingCode0
PIG: Prompt Images Guidance for Night-Time Scene ParsingCode0
Holistic 3D Scene Parsing and Reconstruction from a Single RGB ImageCode0
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature FusionCode0
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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