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 251–300 of 462 papers

TitleStatusHype
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology—0
COCO-OLAC: A Benchmark for Occluded Panoptic Segmentation and Image UnderstandingCode0
Panoptic-Depth Forecasting—0
Resolving Inconsistent Semantics in Multi-Dataset Image Segmentation—0
Dynamic Prompting of Frozen Text-to-Image Diffusion Models for Panoptic Narrative Grounding—0
Towards Localizing Structural Elements: Merging Geometrical Detection with Semantic Verification in RGB-D Data—0
A Simple and Generalist Approach for Panoptic Segmentation—0
DQFormer: Towards Unified LiDAR Panoptic Segmentation with Decoupled Queries—0
SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation—0
MC-PanDA: Mask Confidence for Panoptic Domain AdaptationCode0
Panoptic Segmentation of Mammograms with Text-To-Image Diffusion Model—0
From Easy to Hard: Learning Curricular Shape-aware Features for Robust Panoptic Scene Graph Generation—0
Panoptic Segmentation of Galactic Structures in LSB Images—0
Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Tasks—0
PanopticRecon: Leverage Open-vocabulary Instance Segmentation for Zero-shot Panoptic Reconstruction—0
PanoSSC: Exploring Monocular Panoptic 3D Scene Reconstruction for Autonomous Driving—0
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 Segmentation—0
3rd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation—0
2nd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation—0
An Integrated Framework for Multi-Granular Explanation of Video SummarizationCode0
Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet—0
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies—0
The revenge of BiSeNet: Efficient Multi-Task Image Segmentation—0
COCONut: Modernizing COCO Segmentation—0
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation—0
JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments—0
Using Images as Covariates: Measuring Curb Appeal with Deep Learning—0
Small, Versatile and Mighty: A Range-View Perception Framework—0
Benchmarking the Robustness of Panoptic Segmentation for Automated Driving—0
Generalizable Semantic Vision Query Generation for Zero-shot Panoptic and Semantic Segmentation—0
Generalizable Entity Grounding via Assistance of Large Language Model—0
UrbanGenAI: Reconstructing Urban Landscapes using Panoptic Segmentation and Diffusion Models—0
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation—0
3D Open-Vocabulary Panoptic Segmentation with 2D-3D Vision-Language Distillation—0
Contextual Associated Triplet Queries for Panoptic Scene Graph Generation—0
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular VideoCode0
EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects for Robotic Manipulation—0
Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation—0
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation—0
Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians—0
JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation—0
Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability—0
Self-trained Panoptic Segmentation—0
ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor Simulation—0
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis—0
4D-Former: Multimodal 4D Panoptic Segmentation—0
Panoptic Out-of-Distribution Segmentation—0
Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and LeavesCode0
SimPLR: A Simple and Plain Transformer for Scaling-Efficient Object Detection and Segmentation—0
A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Mask DINO (single scale)PQ59.5—Unverified
2kMaX-DeepLab (single-scale)PQ58.5—Unverified
3Mask2Former (Swin-L)PQ58.3—Unverified
4Panoptic SegFormer (Swin-L)PQ56.2—Unverified
5Panoptic SegFormer (PVTv2-B5)PQ55.8—Unverified
6CMT-DeepLab (single-scale)PQ55.7—Unverified
7K-Net (Swin-L)PQ55.2—Unverified
8MaskConver (ResNet50, single-scale)PQ53.6—Unverified
9MaskFormer (Swin-L)PQ53.3—Unverified
10Panoptic FCN* (Swin-L)PQ52.7—Unverified
#ModelMetricClaimedVerifiedStatus
1HyperSeg (Swin-B)PQ61.2—Unverified
2OneFormer (InternImage-H,single-scale)PQ60—Unverified
3OpenSeeD (SwinL, single-scale)PQ59.5—Unverified
4UMG-CLIP-E/14PQ59.5—Unverified
5MasK DINO (SwinL,single-scale)PQ59.4—Unverified
6EoMT (DINOv2-g, single-scale, 1280x1280)PQ59.2—Unverified
7UMG-CLIP-L/14PQ58.9—Unverified
8Panoptic FCN* (Swin-L, single-scale)PQth58.5—Unverified
9DiNAT-L (single-scale, Mask2Former)PQ58.5—Unverified
10ViT-Adapter-L (single-scale, BEiTv2 pretrain, Mask2Former)PQ58.4—Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer (DiNAT-L, single-scale)PQ46.7—Unverified
2OneFormer (ConvNeXt-L, single-scale)PQ46.4—Unverified
3Panoptic FCN* (Swin-L, single-scale)PQ45.7—Unverified
4Panoptic-DeepLab (SWideRNet-(1, 1, 4.5), multi-scale)PQ44.8—Unverified
5Panoptic FCN* (ResNet-50-FPN)PQst42.3—Unverified
6Mask2Former + Intra-Batch Supervision (ResNet-50)PQ42.2—Unverified
7Axial-DeepLab-L (multi-scale)PQ41.1—Unverified
8EfficientPSPQ40.6—Unverified
9Panoptic-DeepLab (X71)PQ40.5—Unverified
10AdaptIS (ResNeXt-101)PQ40.3—Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer (ConvNeXt-L, single-scale, Mapillary Vistas-Pretrained)PQ68—Unverified
2Panoptic-DeepLab (SWideRNet [1, 1, 4.5], Mapillary, multi-scale)PQ67.8—Unverified
3EfficientPSPQ67.1—Unverified
4Axial-DeepLab-XL (Mapillary Vistas, multi-scale)PQ66.6—Unverified
5kMaX-DeepLab (single-scale)PQ66.2—Unverified
6Panoptic-DeeplabPQ65.5—Unverified
7EfficientPS (Cityscapes-fine)PQ62.9—Unverified
8COPS (ResNet-50)PQ60—Unverified
9SOGNet (ResNet-50)PQ60—Unverified
10Dynamically Instantiated NetworkPQ55.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Mask2Former (Swin-B)PQ41.7—Unverified
2Panoptic FPN (ResNet-50)PQ40.1—Unverified
3Mask2Former (Swin-T)PQ39.2—Unverified
4Panoptic FPN (ResNet-101)PQ38.7—Unverified
5Mask2Former (ResNet-50)PQ37.6—Unverified
6Mask2Former (ResNet-101)PQ37.2—Unverified
7Panoptic Deeplab (ResNet-50)PQ34.7—Unverified
8MaX-DeepLabPQ31.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SuperClusterPQ50.1—Unverified
2PointGroup (Xiang 2023)PQ42.3—Unverified
3KPConv (Xiang 2023)PQ41.8—Unverified
4MinkowskiNet (Xiang 2023)PQ39.2—Unverified
5PointNet++ (Xiang 2023)PQ24.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer3DPQ71.2—Unverified
2PanopticNDT (10cm)PQ59.19—Unverified
3SuperClusterPQ58.7—Unverified
4PanopticFusion (with CRF)PQ33.5—Unverified
5SceneGraphFusion (NN mapping)PQ31.5—Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientPSPQ51.1—Unverified
2SeamlessPQ48.5—Unverified
3UPSNetPQ47.1—Unverified
4Panoptic FPNPQ46.7—Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientPSPQ43.7—Unverified
2SeamlessPQ42.2—Unverified
3UPSNetPQ39.9—Unverified
4Panoptic FPNPQ39.3—Unverified
#ModelMetricClaimedVerifiedStatus
1LKCellPQ50.8—Unverified
2CellViT-SAM-HPQ50.62—Unverified
3TSFDPQ50.4—Unverified
4NuLite-HPQ49.81—Unverified
#ModelMetricClaimedVerifiedStatus
1OneFormer3DPQ71.2—Unverified
2SuperClusterPQ58.7—Unverified
3PanopticFusionPQ33.5—Unverified
4SceneGraphFusionPQ31.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Exchanger+Mask2FormerPQ52.6—Unverified
2Exchanger+Unet+PaPsPQ47.8—Unverified
3U-TAE + PaPsPQ40.4—Unverified
#ModelMetricClaimedVerifiedStatus
1VAN-B6*PQ58.2—Unverified
2PFPN (ideal number of groups)PQ42.15—Unverified
#ModelMetricClaimedVerifiedStatus
1CAFuser (Swin-T)PQ59.7—Unverified
2MUSES (Mask2Former /w 4xSwin-T)PQ53.6—Unverified
#ModelMetricClaimedVerifiedStatus
1EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned)PQ51.15—Unverified
2EMSANetPQ47.38—Unverified
#ModelMetricClaimedVerifiedStatus
1P3FormerPQ0.65—Unverified
2DS-NetPQ0.56—Unverified
#ModelMetricClaimedVerifiedStatus
1MasQCLIPPQ23.3—Unverified