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 201–250 of 462 papers

TitleStatusHype
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic SegmentationCode1
Video Panoptic SegmentationCode1
End-to-End Object Detection with TransformersCode1
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene UnderstandingCode1
EfficientPS: Efficient Panoptic SegmentationCode1
PointGroup: Dual-Set Point Grouping for 3D Instance SegmentationCode1
EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention FusionCode1
CenterMask : Real-Time Anchor-Free Instance SegmentationCode1
SpatialFlow: Bridging All Tasks for Panoptic SegmentationCode1
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature FusionCode1
Weakly- and Semi-Supervised Panoptic SegmentationCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Panoptic SegmentationCode1
Mask R-CNNCode1
DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic SegmentationCode0
OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts—0
HieraSurg: Hierarchy-Aware Diffusion Model for Surgical Video Generation—0
PanSt3R: Multi-view Consistent Panoptic Segmentation—0
Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning—0
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects—0
SydneyScapes: Image Segmentation for Australian Environments—0
Zero-Shot 4D Lidar Panoptic Segmentation—0
PanoGS: Gaussian-based Panoptic Segmentation for 3D Open Vocabulary Scene Understanding—0
ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling—0
Panoptic-CUDAL Technical Report: Rural Australia Point Cloud Dataset in Rainy Conditions—0
3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different SensorsCode0
SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint—0
Learning Appearance and Motion Cues for Panoptic Tracking—0
MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation—0
Label-Efficient LiDAR Panoptic Segmentation—0
Pointmap Association and Piecewise-Plane Constraint for Consistent and Compact 3D Gaussian Segmentation Field—0
COCONut-PanCap: Joint Panoptic Segmentation and Grounded Captions for Fine-Grained Understanding and Generation—0
Improving vision-language alignment with graph spiking hybrid Networks—0
DreamMask: Boosting Open-vocabulary Panoptic Segmentation with Synthetic Data—0
Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and LeavesCode0
LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training—0
Open-World Panoptic Segmentation—0
PanSR: An Object-Centric Mask Transformer for Panoptic SegmentationCode0
Balancing Shared and Task-Specific Representations: A Hybrid Approach to Depth-Aware Video Panoptic Segmentation—0
CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings—0
Panoptic Diffusion Models: co-generation of images and segmentation maps—0
LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable AttentionCode0
Weakly supervised image segmentation for defect-based grading of fresh produceCode0
MGNiceNet: Unified Monocular Geometric Scene UnderstandingCode0
Agricultural Landscape Understanding At Country-Scale—0
PLGS: Robust Panoptic Lifting with 3D Gaussian Splatting—0
Configurable Embodied Data Generation for Class-Agnostic RGB-D Video Segmentation—0
In-Place Panoptic Radiance Field Segmentation with Perceptual Prior for 3D Scene Understanding—0
Adapting Vision-Language Model with Fine-grained Semantics for Open-Vocabulary Segmentation—0
Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks—0
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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