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 351400 of 462 papers

TitleStatusHype
Perceiving the Invisible: Proposal-Free Amodal Panoptic Segmentation0
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes0
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap0
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation0
Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity0
Joint Forecasting of Panoptic Segmentations with Difference AttentionCode0
Panoptic Segmentation using Synthetic and Real Data0
Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation0
ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate ParticlesCode0
Towards On-Board Panoptic Segmentation of Multispectral Satellite Images0
Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor0
PNM: Pixel Null Model for General Image Segmentation0
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation0
Panoptic segmentation with highly imbalanced semantic labels0
Hybrid Tracker with Pixel and Instance for Video Panoptic Segmentation0
Amodal Panoptic Segmentation0
Nuclei panoptic segmentation and composition regression with multi-task deep neural networks0
Automated processing of X-ray computed tomography images via panoptic segmentation for modeling woven composite textiles0
Towards holistic scene understanding: Semantic segmentation and beyond0
Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic Segmentation0
CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation0
Unifying Panoptic Segmentation for Autonomous Driving0
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation0
7th AI Driving Olympics: 1st Place Report for Panoptic Tracking0
Panoptic-aware Image-to-Image Translation0
Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR ScansCode0
Panoptic Segmentation Meets Remote Sensing0
Boosting Supervised Learning Performance with Co-training0
Towards Panoptic 3D Parsing for Single Image in the Wild0
CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds0
An End-to-End Trainable Video Panoptic Segmentation Method usingTransformers0
Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation0
A Compositional Approach to Occlusion in Panoptic Segmentation0
SemIE: Semantically-aware Image Extrapolation0
SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering0
Multi-task learning from fixed-wing UAV images for 2D/3D city modeling0
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network0
SE-PSNet: Silhouette-based Enhancement Feature for Panoptic Segmentation Network0
Merging Tasks for Video Panoptic Segmentation0
SOLO: A Simple Framework for Instance Segmentation0
LPSNet: A Lightweight Solution for Fast Panoptic Segmentation0
Hierarchical Lovasz Embeddings for Proposal-Free Panoptic Segmentation0
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation0
Learning to Associate Every Segment for Video Panoptic Segmentation0
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation0
Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation0
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception0
Panoptic Segmentation Forecasting0
Panoptic Lintention Network: Towards Efficient Navigational Perception for the Visually ImpairedCode0
STEP: Segmenting and Tracking Every Pixel0
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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