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 401450 of 462 papers

TitleStatusHype
Video Panoptic SegmentationCode1
Fast Object Classification and Meaningful Data Representation of Segmented Lidar Instances0
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous ConvolutionCode2
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation0
End-to-End Object Detection with TransformersCode1
Stable and expressive recurrent vision modelsCode0
Panoptic Instance Segmentation on Pigs0
Hierarchical Multi-Scale Attention for Semantic SegmentationCode2
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation0
ResNeSt: Split-Attention NetworksCode3
MOPT: Multi-Object Panoptic Tracking0
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene UnderstandingCode1
Bidirectional Graph Reasoning Network for Panoptic Segmentation0
EfficientPS: Efficient Panoptic SegmentationCode1
Pixel Consensus Voting for Panoptic Segmentation0
PointGroup: Dual-Set Point Grouping for 3D Instance SegmentationCode1
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic SegmentationCode0
EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention FusionCode1
SOLOv2: Dynamic and Fast Instance SegmentationCode2
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic SegmentationCode2
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI0
Towards Bounding-Box Free Panoptic Segmentation0
Unifying Training and Inference for Panoptic Segmentation0
Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey0
Bipartite Conditional Random Fields for Panoptic SegmentationCode0
Real-Time Panoptic Segmentation from Dense Detections0
PanDA: Panoptic Data Augmentation0
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic SegmentationCode0
SOGNet: Scene Overlap Graph Network for Panoptic SegmentationCode0
CenterMask : Real-Time Anchor-Free Instance SegmentationCode1
Single-Shot Panoptic Segmentation0
SpatialFlow: Bridging All Tasks for Panoptic SegmentationCode1
Panoptic-DeepLabCode0
Fast Panoptic Segmentation Network0
DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation0
Connectivity-constrained interactive annotations for panoptic segmentation0
Feature Pyramid Encoding Network for Real-time Semantic Segmentation0
AdaptIS: Adaptive Instance Selection NetworkCode0
Generator evaluator-selector net for panoptic image segmentation and splitting unfamiliar objects into partsCode0
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature FusionCode1
Panoptic Image Annotation with a Collaborative Assistant0
Learning Instance Occlusion for Panoptic SegmentationCode0
Seamless Scene SegmentationCode0
Detecting Reflections by Combining Semantic and Instance Segmentation0
An End-to-End Network for Panoptic Segmentation0
PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things0
Class-independent sequential full image segmentation, using a convolutional net that finds a segment within an attention region, given a pointer pixel within this segmentCode0
DeeperLab: Single-Shot Image Parser0
Single Network Panoptic Segmentation for Street Scene UnderstandingCode0
UPSNet: A Unified Panoptic Segmentation NetworkCode0
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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