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
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic SegmentationCode1
Panoptic 3D Scene Reconstruction From a Single RGB ImageCode1
1st Place Solution for PVUW Challenge 2023: Video Panoptic SegmentationCode1
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic SegmentationCode1
TarViS: A Unified Approach for Target-based Video SegmentationCode1
Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual CitiesCode1
PanopticDepth: A Unified Framework for Depth-aware Panoptic SegmentationCode1
DVIS++: Improved Decoupled Framework for Universal Video SegmentationCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
EDAPS: Enhanced Domain-Adaptive Panoptic SegmentationCode1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Efficient Multi-Task RGB-D Scene Analysis for Indoor EnvironmentsCode1
SpatialFlow: Bridging All Tasks for Panoptic SegmentationCode1
Panoptic Narrative GroundingCode1
Zero-Shot 4D Lidar Panoptic Segmentation0
1st Place Winner of the 2024 Pixel-level Video Understanding in the Wild (CVPR'24 PVUW) Challenge in Video Panoptic Segmentation and Best Long Video Consistency of Video Semantic Segmentation0
2nd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation0
3D detection of roof sections from a single satellite image and application to LOD2-building reconstruction0
3D Open-Vocabulary Panoptic Segmentation with 2D-3D Vision-Language Distillation0
3rd Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation0
3rd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation0
4D-Former: Multimodal 4D Panoptic Segmentation0
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction0
7th AI Driving Olympics: 1st Place Report for Panoptic Tracking0
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI0
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception0
A Compositional Approach to Occlusion in Panoptic Segmentation0
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects0
Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation0
Agricultural Landscape Understanding At Country-Scale0
Amodal Panoptic Segmentation0
An End-to-End Network for Panoptic Segmentation0
An End-to-End Trainable Video Panoptic Segmentation Method usingTransformers0
AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation0
A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition0
A Simple and Generalist Approach for Panoptic Segmentation0
ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor Simulation0
A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction0
Attention-guided Unified Network for Panoptic Segmentation0
Automated processing of X-ray computed tomography images via panoptic segmentation for modeling woven composite textiles0
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation0
Balancing Shared and Task-Specific Representations: A Hybrid Approach to Depth-Aware Video Panoptic Segmentation0
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology0
Benchmarking the Robustness of Panoptic Segmentation for Automated Driving0
Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation0
Bidirectional Graph Reasoning Network for Panoptic Segmentation0
Boosting Supervised Learning Performance with Co-training0
CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings0
Can we cover navigational perception needs of the visually impaired by panoptic segmentation?0
CASNet: Common Attribute Support Network for image instance and panoptic segmentation0
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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
3UMG-CLIP-E/14PQ59.5Unverified
4OpenSeeD (SwinL, single-scale)PQ59.5Unverified
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