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 301350 of 462 papers

TitleStatusHype
SANPO: A Scene Understanding, Accessibility and Human Navigation Dataset0
PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics0
Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation0
SportsSloMo: A New Benchmark and Baselines for Human-centric Video Frame Interpolation0
Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities0
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding0
3D detection of roof sections from a single satellite image and application to LOD2-building reconstruction0
Test-Time Training on Video Streams0
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation0
Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving ScenesCode0
3rd Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation0
SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation NetworkCode0
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural NetworkCode0
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
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation0
Uncertainty estimation in Deep Learning for Panoptic segmentation0
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation0
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction0
Open-vocabulary Panoptic Segmentation with Embedding Modulation0
Towards Universal Vision-language Omni-supervised Segmentation0
Rethinking Range View Representation for LiDAR Segmentation0
Nerflets: Local Radiance Fields for Efficient Structure-Aware 3D Scene Representation from 2D Supervision0
Deep Learning Based Dominant Index Lesion Segmentation for MR-guided Radiation Therapy of Prostate Cancer0
Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs0
LMSeg: Language-guided Multi-dataset Segmentation0
Tuning computer vision models with task rewards0
PUPS: Point Cloud Unified Panoptic Segmentation0
On the Ideal Number of Groups for Isometric Gradient Propagation0
AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation0
Panoptic Compositional Feature Field for Editable Scene Rendering With Network-Inferred Labels via Metric Learning0
SegGPT: Towards Segmenting Everything in Context0
Connectivity-constrained Interactive Panoptic Segmentation0
CoMFormer: Continual Learning in Semantic and Panoptic Segmentation0
Dual Complementary Dynamic Convolution for Image Recognition0
MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation0
A Generalist Framework for Panoptic Segmentation of Images and Videos0
Uncertainty-aware LiDAR Panoptic SegmentationCode0
Time-Space Transformers for Video Panoptic Segmentation0
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception0
Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower SpeciesCode0
SUNet: Scale-aware Unified Network for Panoptic Segmentation0
Dual Decision Improves Open-Set Panoptic Segmentation0
A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction0
UniDAformer: Unified Domain Adaptive Panoptic Segmentation Transformer via Hierarchical Mask Calibration0
MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation0
LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network0
Waymo Open Dataset: Panoramic Video Panoptic Segmentation0
TubeFormer-DeepLab: Video Mask Transformer0
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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