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
FoodSAM: Any Food SegmentationCode1
Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities0
LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware AlignmentCode1
Point2Mask: Point-supervised Panoptic Segmentation via Optimal TransportCode1
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding0
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance LearningCode1
Learning Dynamic Query Combinations for Transformer-based Object Detection and SegmentationCode1
A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and FutureCode2
On Point Affiliation in Feature UpsamplingCode1
Pair then Relation: Pair-Net for Panoptic Scene Graph GenerationCode1
Test-Time Training on Video Streams0
3D detection of roof sections from a single satellite image and application to LOD2-building reconstruction0
Towards accurate instance segmentation in large-scale LiDAR point cloudsCode1
Hierarchical Open-vocabulary Universal Image SegmentationCode2
ReMaX: Relaxing for Better Training on Efficient Panoptic SegmentationCode0
PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and AggregationCode1
CellViT: Vision Transformers for Precise Cell Segmentation and ClassificationCode2
Faster Segment Anything: Towards Lightweight SAM for Mobile ApplicationsCode5
Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving ScenesCode0
Primitive Generation and Semantic-related Alignment for Universal Zero-Shot SegmentationCode1
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic SegmentationCode1
3rd Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation0
Efficient Multi-Task Scene Analysis with RGB-D TransformersCode1
1st Place Solution for PVUW Challenge 2023: Video Panoptic SegmentationCode1
PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural DomainCode1
DFormer: Diffusion-guided Transformer for Universal Image SegmentationCode1
BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single ImageCode1
SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation NetworkCode0
SAD: Segment Any RGBDCode2
Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language ModelCode2
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural NetworkCode0
Revisiting the Encoding of Satellite Image Time SeriesCode1
CLUSTSEG: Clustering for Universal SegmentationCode1
RT-K-Net: Revisiting K-Net for Real-Time Panoptic SegmentationCode1
EDAPS: Enhanced Domain-Adaptive Panoptic SegmentationCode1
A Review of Panoptic Segmentation for Mobile Mapping Point CloudsCode1
AutoFocusFormer: Image Segmentation off the GridCode1
Ensembling Instance and Semantic Segmentation for Panoptic Segmentation0
ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic Segmentation0
Intra-Batch Supervision for Panoptic Segmentation on High-Resolution ImagesCode0
An Instance Segmentation Dataset of Yeast Cells in MicrostructuresCode1
Instance Neural Radiance FieldCode1
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation0
SegGPT: Segmenting Everything In ContextCode4
Uncertainty estimation in Deep Learning for Panoptic segmentation0
FinnWoodlands DatasetCode1
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation0
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction0
You Only Segment Once: Towards Real-Time Panoptic SegmentationCode2
Position-Guided Point Cloud Panoptic Segmentation TransformerCode1
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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
3OpenSeeD (SwinL, single-scale)PQ59.5Unverified
4UMG-CLIP-E/14PQ59.5Unverified
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