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 101–150 of 462 papers

TitleStatusHype
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic SegmentationCode1
PanopticNDT: Efficient and Robust Panoptic MappingCode1
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part SegmentationCode1
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR SegmentationCode1
K-Net: Towards Unified Image SegmentationCode1
Large-batch Optimization for Dense Visual PredictionsCode1
A Good Foundation is Worth Many Labels: Label-Efficient Panoptic SegmentationCode1
Mask R-CNNCode1
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask TransformersCode1
Large-Scale Video Panoptic Segmentation in the Wild: A BenchmarkCode1
CMT-DeepLab: Clustering Mask Transformers for Panoptic SegmentationCode1
CLUSTSEG: Clustering for Universal SegmentationCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Lidar Panoptic Segmentation and Tracking without Bells and WhistlesCode1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Learning Dynamic Query Combinations for Transformer-based Object Detection and SegmentationCode1
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time SeriesCode1
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under UncertaintyCode1
HCFormer: Unified Image Segmentation with Hierarchical ClusteringCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal ModelCode1
kMaX-DeepLab: k-means Mask TransformerCode1
EDAPS: Enhanced Domain-Adaptive Panoptic SegmentationCode1
4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and AggregationCode1
Efficient Multi-Task RGB-D Scene Analysis for Indoor EnvironmentsCode1
Efficient Multi-Task Scene Analysis with RGB-D TransformersCode1
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and BenchmarkCode1
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part SegmentationCode1
Instance Neural Radiance FieldCode1
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene UnderstandingCode1
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual ReferringCode1
A Review of Panoptic Segmentation for Mobile Mapping Point CloudsCode1
CenterMask : Real-Time Anchor-Free Instance SegmentationCode1
Improving Video Instance Segmentation via Temporal Pyramid RoutingCode1
Center Focusing Network for Real-Time LiDAR Panoptic SegmentationCode1
CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and ClassificationCode1
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic SegmentationCode1
Few-Shot Panoptic Segmentation With Foundation ModelsCode1
Improving Sketch Colorization using Adversarial Segmentation ConsistencyCode1
PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and AggregationCode1
Panoptic 3D Scene Reconstruction From a Single RGB ImageCode1
PanopticDepth: A Unified Framework for Depth-aware Panoptic SegmentationCode1
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
An Instance Segmentation Dataset of Yeast Cells in MicrostructuresCode1
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object InteractionCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
FinnWoodlands DatasetCode1
FlexiViT: One Model for All Patch SizesCode1
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Mask DINO (single scale)PQ59.5—Unverified
2kMaX-DeepLab (single-scale)PQ58.5—Unverified
3Mask2Former (Swin-L)PQ58.3—Unverified
4Panoptic SegFormer (Swin-L)PQ56.2—Unverified
5Panoptic SegFormer (PVTv2-B5)PQ55.8—Unverified
6CMT-DeepLab (single-scale)PQ55.7—Unverified
7K-Net (Swin-L)PQ55.2—Unverified
8MaskConver (ResNet50, single-scale)PQ53.6—Unverified
9MaskFormer (Swin-L)PQ53.3—Unverified
10Panoptic FCN* (Swin-L)PQ52.7—Unverified
#ModelMetricClaimedVerifiedStatus
1HyperSeg (Swin-B)PQ61.2—Unverified
2OneFormer (InternImage-H,single-scale)PQ60—Unverified
3OpenSeeD (SwinL, single-scale)PQ59.5—Unverified
4UMG-CLIP-E/14PQ59.5—Unverified
5MasK DINO (SwinL,single-scale)PQ59.4—Unverified
6EoMT (DINOv2-g, single-scale, 1280x1280)PQ59.2—Unverified
7UMG-CLIP-L/14PQ58.9—Unverified
8Panoptic FCN* (Swin-L, single-scale)PQth58.5—Unverified
9DiNAT-L (single-scale, Mask2Former)PQ58.5—Unverified
10ViT-Adapter-L (single-scale, BEiTv2 pretrain, Mask2Former)PQ58.4—Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer (DiNAT-L, single-scale)PQ46.7—Unverified
2OneFormer (ConvNeXt-L, single-scale)PQ46.4—Unverified
3Panoptic FCN* (Swin-L, single-scale)PQ45.7—Unverified
4Panoptic-DeepLab (SWideRNet-(1, 1, 4.5), multi-scale)PQ44.8—Unverified
5Panoptic FCN* (ResNet-50-FPN)PQst42.3—Unverified
6Mask2Former + Intra-Batch Supervision (ResNet-50)PQ42.2—Unverified
7Axial-DeepLab-L (multi-scale)PQ41.1—Unverified
8EfficientPSPQ40.6—Unverified
9Panoptic-DeepLab (X71)PQ40.5—Unverified
10AdaptIS (ResNeXt-101)PQ40.3—Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer (ConvNeXt-L, single-scale, Mapillary Vistas-Pretrained)PQ68—Unverified
2Panoptic-DeepLab (SWideRNet [1, 1, 4.5], Mapillary, multi-scale)PQ67.8—Unverified
3EfficientPSPQ67.1—Unverified
4Axial-DeepLab-XL (Mapillary Vistas, multi-scale)PQ66.6—Unverified
5kMaX-DeepLab (single-scale)PQ66.2—Unverified
6Panoptic-DeeplabPQ65.5—Unverified
7EfficientPS (Cityscapes-fine)PQ62.9—Unverified
8COPS (ResNet-50)PQ60—Unverified
9SOGNet (ResNet-50)PQ60—Unverified
10Dynamically Instantiated NetworkPQ55.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Mask2Former (Swin-B)PQ41.7—Unverified
2Panoptic FPN (ResNet-50)PQ40.1—Unverified
3Mask2Former (Swin-T)PQ39.2—Unverified
4Panoptic FPN (ResNet-101)PQ38.7—Unverified
5Mask2Former (ResNet-50)PQ37.6—Unverified
6Mask2Former (ResNet-101)PQ37.2—Unverified
7Panoptic Deeplab (ResNet-50)PQ34.7—Unverified
8MaX-DeepLabPQ31.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SuperClusterPQ50.1—Unverified
2PointGroup (Xiang 2023)PQ42.3—Unverified
3KPConv (Xiang 2023)PQ41.8—Unverified
4MinkowskiNet (Xiang 2023)PQ39.2—Unverified
5PointNet++ (Xiang 2023)PQ24.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer3DPQ71.2—Unverified
2PanopticNDT (10cm)PQ59.19—Unverified
3SuperClusterPQ58.7—Unverified
4PanopticFusion (with CRF)PQ33.5—Unverified
5SceneGraphFusion (NN mapping)PQ31.5—Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientPSPQ51.1—Unverified
2SeamlessPQ48.5—Unverified
3UPSNetPQ47.1—Unverified
4Panoptic FPNPQ46.7—Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientPSPQ43.7—Unverified
2SeamlessPQ42.2—Unverified
3UPSNetPQ39.9—Unverified
4Panoptic FPNPQ39.3—Unverified
#ModelMetricClaimedVerifiedStatus
1LKCellPQ50.8—Unverified
2CellViT-SAM-HPQ50.62—Unverified
3TSFDPQ50.4—Unverified
4NuLite-HPQ49.81—Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer3DPQ71.2—Unverified
2SuperClusterPQ58.7—Unverified
3PanopticFusionPQ33.5—Unverified
4SceneGraphFusionPQ31.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Exchanger+Mask2FormerPQ52.6—Unverified
2Exchanger+Unet+PaPsPQ47.8—Unverified
3U-TAE + PaPsPQ40.4—Unverified
#ModelMetricClaimedVerifiedStatus
1VAN-B6*PQ58.2—Unverified
2PFPN (ideal number of groups)PQ42.15—Unverified
#ModelMetricClaimedVerifiedStatus
1CAFuser (Swin-T)PQ59.7—Unverified
2MUSES (Mask2Former /w 4xSwin-T)PQ53.6—Unverified
#ModelMetricClaimedVerifiedStatus
1EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned)PQ51.15—Unverified
2EMSANetPQ47.38—Unverified
#ModelMetricClaimedVerifiedStatus
1P3FormerPQ0.65—Unverified
2DS-NetPQ0.56—Unverified
#ModelMetricClaimedVerifiedStatus
1MasQCLIPPQ23.3—Unverified