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 51–75 of 199 papers

TitleStatusHype
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
Radar Spectra-Language Model for Automotive Scene Parsing—0
Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024—0
Few-Shot Fruit Segmentation via Transfer LearningCode0
Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks—0
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature FusionCode0
Feature boosting with efficient attention for scene parsing—0
Applying Unsupervised Semantic Segmentation to High-Resolution UAV Imagery for Enhanced Road Scene ParsingCode0
LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field CamerasCode0
SAI3D: Segment Any Instance in 3D Scenes—0
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 ParsingCode0
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
Local and Global Contextual Features Fusion for Pedestrian Intention Prediction—0
Weakly Supervised Class-Agnostic Motion Prediction for Autonomous Driving—0
Re:PolyWorld - A Graph Neural Network for Polygonal Scene Parsing—0
Visual Traffic Knowledge Graph Generation from Scene Images—0
Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection—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