SOTAVerified

Panoptic Segmentation

Panoptic Segmentation is a computer vision task that combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. The goal of panoptic segmentation is to segment the image into semantically meaningful parts or regions, while also detecting and distinguishing individual instances of objects within those regions. In a given image, every pixel is assigned a semantic label, and pixels belonging to "things" classes (countable objects with instances, like cars and people) are assigned unique instance IDs. ( Image credit: Detectron2 )

Papers

Showing 151200 of 462 papers

TitleStatusHype
Mask4Former: Mask Transformer for 4D Panoptic SegmentationCode1
RankSeg: Adaptive Pixel Classification with Image Category Ranking for SegmentationCode1
Fully Convolutional Networks for Panoptic SegmentationCode1
Fully Convolutional Networks for Panoptic Segmentation with Point-based SupervisionCode1
Robust Double-Encoder Network for RGB-D Panoptic SegmentationCode1
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask TransformersCode1
Lidar Panoptic Segmentation in an Open WorldCode1
MGNet: Monocular Geometric Scene Understanding for Autonomous DrivingCode1
Lite Vision Transformer with Enhanced Self-AttentionCode1
LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware AlignmentCode1
An Instance Segmentation Dataset of Yeast Cells in MicrostructuresCode1
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
Lidar Panoptic Segmentation and Tracking without Bells and WhistlesCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution KernelsCode1
NuLite -- Lightweight and Fast Model for Nuclei Instance Segmentation and ClassificationCode1
BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single ImageCode1
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuningCode1
LiDAR-based Panoptic Segmentation via Dynamic Shifting NetworkCode1
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object InteractionCode1
Segmenting Known Objects and Unseen Unknowns without Prior KnowledgeCode1
How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic SegmentationCode1
One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection TasksCode1
Exemplar-Based Open-Set Panoptic Segmentation NetworkCode1
Building a Strong Pre-Training Baseline for Universal 3D Large-Scale PerceptionCode1
PVT v2: Improved Baselines with Pyramid Vision TransformerCode1
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
Improving Sketch Colorization using Adversarial Segmentation ConsistencyCode1
Boundary IoU: Improving Object-Centric Image Segmentation EvaluationCode1
EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention FusionCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Pair then Relation: Pair-Net for Panoptic Scene Graph GenerationCode1
Panoptic Narrative GroundingCode1
Instance Neural Radiance FieldCode1
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual ReferringCode1
EOV-Seg: Efficient Open-Vocabulary Panoptic SegmentationCode1
Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI -Should Different Clinical Objectives Mandate Different Loss Functions?Code1
MSeg: A Composite Dataset for Multi-domain Semantic SegmentationCode1
PanopticDepth: A Unified Framework for Depth-aware Panoptic SegmentationCode1
End-to-End Object Detection with TransformersCode1
ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR DataCode1
kMaX-DeepLab: k-means Mask TransformerCode1
K-Net: Towards Unified Image SegmentationCode1
Context-Aware Relative Object Queries To Unify Video Instance and Panoptic SegmentationCode1
Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View ImagesCode1
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part SegmentationCode1
Large-batch Optimization for Dense Visual PredictionsCode1
Large-Scale Video Panoptic Segmentation in the Wild: A BenchmarkCode1
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and BenchmarkCode1
EfficientPS: Efficient Panoptic SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Mask DINO (single scale)PQ59.5Unverified
2kMaX-DeepLab (single-scale)PQ58.5Unverified
3Mask2Former (Swin-L)PQ58.3Unverified
4Panoptic SegFormer (Swin-L)PQ56.2Unverified
5Panoptic SegFormer (PVTv2-B5)PQ55.8Unverified
6CMT-DeepLab (single-scale)PQ55.7Unverified
7K-Net (Swin-L)PQ55.2Unverified
8MaskConver (ResNet50, single-scale)PQ53.6Unverified
9MaskFormer (Swin-L)PQ53.3Unverified
10Panoptic FCN* (Swin-L)PQ52.7Unverified
#ModelMetricClaimedVerifiedStatus
1HyperSeg (Swin-B)PQ61.2Unverified
2OneFormer (InternImage-H,single-scale)PQ60Unverified
3UMG-CLIP-E/14PQ59.5Unverified
4OpenSeeD (SwinL, single-scale)PQ59.5Unverified
5MasK DINO (SwinL,single-scale)PQ59.4Unverified
6EoMT (DINOv2-g, single-scale, 1280x1280)PQ59.2Unverified
7UMG-CLIP-L/14PQ58.9Unverified
8Panoptic FCN* (Swin-L, single-scale)PQth58.5Unverified
9DiNAT-L (single-scale, Mask2Former)PQ58.5Unverified
10ViT-Adapter-L (single-scale, BEiTv2 pretrain, Mask2Former)PQ58.4Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer (DiNAT-L, single-scale)PQ46.7Unverified
2OneFormer (ConvNeXt-L, single-scale)PQ46.4Unverified
3Panoptic FCN* (Swin-L, single-scale)PQ45.7Unverified
4Panoptic-DeepLab (SWideRNet-(1, 1, 4.5), multi-scale)PQ44.8Unverified
5Panoptic FCN* (ResNet-50-FPN)PQst42.3Unverified
6Mask2Former + Intra-Batch Supervision (ResNet-50)PQ42.2Unverified
7Axial-DeepLab-L (multi-scale)PQ41.1Unverified
8EfficientPSPQ40.6Unverified
9Panoptic-DeepLab (X71)PQ40.5Unverified
10AdaptIS (ResNeXt-101)PQ40.3Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer (ConvNeXt-L, single-scale, Mapillary Vistas-Pretrained)PQ68Unverified
2Panoptic-DeepLab (SWideRNet [1, 1, 4.5], Mapillary, multi-scale)PQ67.8Unverified
3EfficientPSPQ67.1Unverified
4Axial-DeepLab-XL (Mapillary Vistas, multi-scale)PQ66.6Unverified
5kMaX-DeepLab (single-scale)PQ66.2Unverified
6Panoptic-DeeplabPQ65.5Unverified
7EfficientPS (Cityscapes-fine)PQ62.9Unverified
8COPS (ResNet-50)PQ60Unverified
9SOGNet (ResNet-50)PQ60Unverified
10Dynamically Instantiated NetworkPQ55.4Unverified
#ModelMetricClaimedVerifiedStatus
1Mask2Former (Swin-B)PQ41.7Unverified
2Panoptic FPN (ResNet-50)PQ40.1Unverified
3Mask2Former (Swin-T)PQ39.2Unverified
4Panoptic FPN (ResNet-101)PQ38.7Unverified
5Mask2Former (ResNet-50)PQ37.6Unverified
6Mask2Former (ResNet-101)PQ37.2Unverified
7Panoptic Deeplab (ResNet-50)PQ34.7Unverified
8MaX-DeepLabPQ31.9Unverified
#ModelMetricClaimedVerifiedStatus
1SuperClusterPQ50.1Unverified
2PointGroup (Xiang 2023)PQ42.3Unverified
3KPConv (Xiang 2023)PQ41.8Unverified
4MinkowskiNet (Xiang 2023)PQ39.2Unverified
5PointNet++ (Xiang 2023)PQ24.6Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer3DPQ71.2Unverified
2PanopticNDT (10cm)PQ59.19Unverified
3SuperClusterPQ58.7Unverified
4PanopticFusion (with CRF)PQ33.5Unverified
5SceneGraphFusion (NN mapping)PQ31.5Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientPSPQ51.1Unverified
2SeamlessPQ48.5Unverified
3UPSNetPQ47.1Unverified
4Panoptic FPNPQ46.7Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientPSPQ43.7Unverified
2SeamlessPQ42.2Unverified
3UPSNetPQ39.9Unverified
4Panoptic FPNPQ39.3Unverified
#ModelMetricClaimedVerifiedStatus
1LKCellPQ50.8Unverified
2CellViT-SAM-HPQ50.62Unverified
3TSFDPQ50.4Unverified
4NuLite-HPQ49.81Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer3DPQ71.2Unverified
2SuperClusterPQ58.7Unverified
3PanopticFusionPQ33.5Unverified
4SceneGraphFusionPQ31.5Unverified
#ModelMetricClaimedVerifiedStatus
1Exchanger+Mask2FormerPQ52.6Unverified
2Exchanger+Unet+PaPsPQ47.8Unverified
3U-TAE + PaPsPQ40.4Unverified
#ModelMetricClaimedVerifiedStatus
1VAN-B6*PQ58.2Unverified
2PFPN (ideal number of groups)PQ42.15Unverified
#ModelMetricClaimedVerifiedStatus
1CAFuser (Swin-T)PQ59.7Unverified
2MUSES (Mask2Former /w 4xSwin-T)PQ53.6Unverified
#ModelMetricClaimedVerifiedStatus
1EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned)PQ51.15Unverified
2EMSANetPQ47.38Unverified
#ModelMetricClaimedVerifiedStatus
1P3FormerPQ0.65Unverified
2DS-NetPQ0.56Unverified
#ModelMetricClaimedVerifiedStatus
1MasQCLIPPQ23.3Unverified