SOTAVerified

Scene Segmentation

Scene segmentation is the task of splitting a scene into its various object components.

Image adapted from Temporally coherent 4D reconstruction of complex dynamic scenes.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 283 papers

TitleStatusHype
Hierarchical Knowledge Distillation for Dialogue Sequence Labeling—0
DSPoint: Dual-scale Point Cloud Recognition with High-frequency FusionCode1
Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion—0
FBNet: Feature Balance Network for Urban-Scene Segmentation—0
FTNet: Feature Transverse Network for Thermal Image Semantic SegmentationCode1
Multi-Domain Incremental Learning for Semantic SegmentationCode1
2020 CATARACTS Semantic Segmentation Challenge—0
Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data—0
Boundary-aware Pre-training for Video Scene Segmentation—0
Predicting Driver Self-Reported Stress by Analyzing the Road Scene—0
CondNet: Conditional Classifier for Scene SegmentationCode1
PDFNet: Pointwise Dense Flow Network for Urban-Scene SegmentationCode0
UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene ImageryCode2
A Multimodal Framework for Video Ads Understanding—0
Semantic Scene Segmentation for Robotics Applications—0
Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation AssistanceCode1
BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene SegmentationCode0
Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View ImagesCode1
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene SegmentationCode1
Unsupervised Discovery of Object Radiance FieldsCode1
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained ModelsCode1
Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos—0
Potential Convolution: Embedding Point Clouds into Potential Fields—0
Detecting Scenes in Fiction: A new Segmentation Task—0
Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric SegmentationCode0
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene SegmentationCode1
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective WhiteningCode1
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene SegmentationCode0
Arthroscopic Multi-Spectral Scene Segmentation Using Deep Learning—0
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents—0
Global-Local Propagation Network for RGB-D Semantic Segmentation—0
Simplifying Object Segmentation with PixelLib LibraryCode2
PARTS: Unsupervised Segmentation With Slots, Attention and Independence Maximization—0
Learning Representation in Colour Conversion—0
Point TransformerCode1
Multi-Model Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation—0
ODFNet: Using orientation distribution functions to characterize 3D point cloudsCode0
Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data—0
The Utility of Decorrelating Colour Spaces in Vector Quantised Variational AutoencodersCode0
Multi-scale Attention U-Net (MsAUNet): A Modified U-Net Architecture for Scene Segmentation—0
SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace DetectionCode1
Scene Segmentation with Dual Relation-aware Attention NetworkCode1
Learning and Reasoning with the Graph Structure Representation in Robotic SurgeryCode1
Learning Physical Graph Representations from Visual ScenesCode1
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene SegmentationCode0
Taskology: Utilizing Task Relations at Scale—0
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes—0
Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and Improved Model Fitting—0
Real-Time Segmentation Networks should be Latency Aware—0
A Local-to-Global Approach to Multi-modal Movie Scene SegmentationCode1
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All datasetsSUN-RGBDScanNetStreetHazardsMovieNetUAVid

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICMMean IoU50.6—Unverified
2Index NetworkMean IoU33.48—Unverified
3DeepLab-LargeFOVMean IoU32.08—Unverified
4SegNetMean IoU31.84—Unverified
5FCNMean IoU27.39—Unverified
#ModelMetricClaimedVerifiedStatus
13DMVAverage Accuracy75—Unverified
2KPConv3DIoU68.6—Unverified
3PointNet++Average Accuracy60.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Mask2AnomalyOpen-mIoU59.8—Unverified
2LDN121-RPLOpen-mIoU56.3—Unverified
3LDN121-DenseHybridOpen-mIoU45.8—Unverified
#ModelMetricClaimedVerifiedStatus
1NeighborNetAP71.9—Unverified
2TranS4merAP60.78—Unverified
#ModelMetricClaimedVerifiedStatus
1UNetFormerCategory mIoU67.8—Unverified