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 101–150 of 199 papers

TitleStatusHype
Cross-CBAM: A Lightweight network for Scene Segmentation—0
Cutting Edge: Soft Correspondences in Multimodal Scene Parsing—0
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling—0
Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation—0
Deep Deconvolutional Networks for Scene Parsing—0
Deep Hierarchical Parsing for Semantic Segmentation—0
Deep Multiphase Level Set for Scene Parsing—0
Deep Semantics-Aware Photo Adjustment—0
Deep Structured Scene Parsing by Learning with Image Descriptions—0
DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization—0
Dermoscopic Image Analysis for ISIC Challenge 2018—0
Differentiating Features for Scene Segmentation Based on Dedicated Attention Mechanisms—0
Discriminative Map Retrieval Using View-Dependent Map Descriptor—0
ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation—0
ESCNet: Gaze Target Detection With the Understanding of 3D Scenes—0
Exploiting the Transferability of Deep Learning Systems Across Multi-modal Retinal Scans for Extracting Retinopathy Lesions—0
Exemplar-Based Face Parsing—0
Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing—0
Feature boosting with efficient attention for scene parsing—0
Finite State Machines for Semantic Scene Parsing and Segmentation—0
RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion—0
RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing—0
SAI3D: Segment Any Instance in 3D Scenes—0
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering—0
Scale-Adaptive Convolutions for Scene Parsing—0
Scene Parsing by Integrating Function, Geometry and Appearance Models—0
Scene Parsing Through ADE20K Dataset—0
Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection—0
Scene Parsing with Integration of Parametric and Non-parametric Models—0
Scene Parsing with Object Instances and Occlusion Ordering—0
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction—0
Cooperative Semantic Segmentation and Image Restoration in Adverse Environmental Conditions—0
Segmenting Ships in Satellite Imagery With Squeeze and Excitation U-Net—0
Segmenting Sky Pixels in Images—0
Semantic Segmentation on VSPW Dataset through Aggregation of Transformer Models—0
Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach—0
Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024—0
Single-View 3D Scene Parsing by Attributed Grammar—0
Soft Correspondences in Multimodal Scene Parsing—0
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
Spatially Constrained Location Prior for Scene Parsing—0
SPGNet: Semantic Prediction Guidance for Scene Parsing—0
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More—0
TBN-ViT: Temporal Bilateral Network with Vision Transformer for Video Scene Parsing—0
Hardware implementation of timely reliable Bayesian decision-making using memristors—0
Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing—0
Variational Context-Deformable ConvNets for Indoor Scene Parsing—0
Video Scene Parsing with Predictive Feature Learning—0
Visual Traffic Knowledge Graph Generation from Scene Images—0
VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild—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