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 51100 of 462 papers

TitleStatusHype
Masked-attention Mask Transformer for Universal Image SegmentationCode2
1st Place Solution for PSG competition with ECCV'22 SenseHuman WorkshopCode2
Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language ModelCode2
Dilated Neighborhood Attention TransformerCode2
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and TrackingCode2
SOLOv2: Dynamic and Fast Instance SegmentationCode2
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg CodebaseCode2
4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and AggregationCode1
LiDAR-based Panoptic Segmentation via Dynamic Shifting NetworkCode1
Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance SegmentationCode1
LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware AlignmentCode1
Large-Scale Video Panoptic Segmentation in the Wild: A BenchmarkCode1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and BenchmarkCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Large-batch Optimization for Dense Visual PredictionsCode1
Learning to Upsample by Learning to SampleCode1
Lidar Panoptic Segmentation and Tracking without Bells and WhistlesCode1
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual ReferringCode1
A Good Foundation is Worth Many Labels: Label-Efficient Panoptic SegmentationCode1
Instance Neural Radiance FieldCode1
kMaX-DeepLab: k-means Mask TransformerCode1
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic SegmentationCode1
A Fair Ranking and New Model for Panoptic Scene Graph GenerationCode1
Improving Sketch Colorization using Adversarial Segmentation ConsistencyCode1
AutoFocusFormer: Image Segmentation off the GridCode1
Adversarial Segmentation Loss for Sketch ColorizationCode1
1st Place Solution for PVUW Challenge 2023: Video Panoptic SegmentationCode1
Improving Video Instance Segmentation via Temporal Pyramid RoutingCode1
K-Net: Towards Unified Image SegmentationCode1
Lidar Panoptic Segmentation in an Open WorldCode1
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic SegmentationCode1
CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving ScenesCode1
GIVT: Generative Infinite-Vocabulary TransformersCode1
Fully Convolutional Networks for Panoptic Segmentation with Point-based SupervisionCode1
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR SegmentationCode1
Fully Convolutional Networks for Panoptic SegmentationCode1
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object InteractionCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
FinnWoodlands DatasetCode1
A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic SegmentationCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
FlexiViT: One Model for All Patch SizesCode1
Context-Aware Relative Object Queries To Unify Video Instance and Panoptic SegmentationCode1
CMT-DeepLab: Clustering Mask Transformers for Panoptic SegmentationCode1
CLUSTSEG: Clustering for Universal SegmentationCode1
HCFormer: Unified Image Segmentation with Hierarchical ClusteringCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
Show:102550
← PrevPage 2 of 10Next →

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