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 201250 of 462 papers

TitleStatusHype
Instance Neural Radiance FieldCode1
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual ReferringCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
TSFD-Net: Tissue specific feature distillation network for nuclei segmentation and classificationCode1
HCFormer: Unified Image Segmentation with Hierarchical ClusteringCode1
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World UnderstandingCode1
Uncertainty-aware Panoptic SegmentationCode1
CLUSTSEG: Clustering for Universal SegmentationCode1
Uni-3D: A Universal Model for Panoptic 3D Scene ReconstructionCode1
Uni-DVPS: Unified Model for Depth-Aware Video Panoptic SegmentationCode1
kMaX-DeepLab: k-means Mask TransformerCode1
K-Net: Towards Unified Image SegmentationCode1
CMT-DeepLab: Clustering Mask Transformers for Panoptic SegmentationCode1
4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and AggregationCode1
SOGNet: Scene Overlap Graph Network for Panoptic SegmentationCode0
Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR ScansCode0
Single Network Panoptic Segmentation for Street Scene UnderstandingCode0
Stable and expressive recurrent vision modelsCode0
STEP: Segmenting and Tracking Every PixelCode0
UPSNet: A Unified Panoptic Segmentation NetworkCode0
Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving ScenesCode0
MGNiceNet: Unified Monocular Geometric Scene UnderstandingCode0
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic SegmentationCode0
UViM: A Unified Modeling Approach for Vision with Learned Guiding CodesCode0
A Generalist Framework for Panoptic Segmentation of Images and VideosCode0
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic SegmentationCode0
ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate ParticlesCode0
MC-PanDA: Mask Confidence for Panoptic Domain AdaptationCode0
Generator evaluator-selector net for panoptic image segmentation and splitting unfamiliar objects into partsCode0
SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation NetworkCode0
ReMaX: Relaxing for Better Training on Efficient Panoptic SegmentationCode0
PanSR: An Object-Centric Mask Transformer for Panoptic SegmentationCode0
Waymo Open Dataset: Panoramic Video Panoptic SegmentationCode0
Class-independent sequential full image segmentation, using a convolutional net that finds a segment within an attention region, given a pointer pixel within this segmentCode0
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular VideoCode0
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene SegmentationCode0
Weakly supervised image segmentation for defect-based grading of fresh produceCode0
3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different SensorsCode0
COCO-OLAC: A Benchmark for Occluded Panoptic Segmentation and Image UnderstandingCode0
LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable AttentionCode0
Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and LeavesCode0
Intra-Batch Supervision for Panoptic Segmentation on High-Resolution ImagesCode0
Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and LeavesCode0
An Integrated Framework for Multi-Granular Explanation of Video SummarizationCode0
DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic SegmentationCode0
Joint Forecasting of Panoptic Segmentations with Difference AttentionCode0
Tuning computer vision models with task rewardsCode0
Learning Instance Occlusion for Panoptic SegmentationCode0
Learning Panoptic Segmentation from Instance ContoursCode0
Uncertainty-aware LiDAR Panoptic SegmentationCode0
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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