Browse State-of-the-Art › Panoptic Segmentation

Panoptic Segmentation

257 papers with code · 27 benchmarks · 37 datasets archive 2025-07-28

Computer Vision

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 )

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

27 leaderboard tables shown for this task, 27 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 27 until expanded.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
COCO test-dev (38 rows) Mask DINO (single scale) Mask DINO: Towards A Unified Transformer-based Framework for... code Syntology ran 11 of 13 samples · 2 unverified Compare
Cityscapes val (37 rows) ViT-P (OneFormer, InternImage-H) The Missing Point in Vision Transformers for Universal Image Segmentation code — Compare
COCO minival (31 rows) HyperSeg (Swin-B) HyperSeg: Towards Universal Visual Segmentation with Large Language Model code Syntology ran 7 of 17 samples · 10 unverified Compare
ADE20K val (25 rows) OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896) OneFormer: One Transformer to Rule Universal Image Segmentation code Syntology ran 0 of 5 samples · 5 unverified Compare
Mapillary val (13 rows) OneFormer (DiNAT-L, single-scale) OneFormer: One Transformer to Rule Universal Image Segmentation code Syntology ran 0 of 5 samples · 5 unverified Compare
Cityscapes test (10 rows) OneFormer (ConvNeXt-L, single-scale, Mapillary Vistas-Pretrained) OneFormer: One Transformer to Rule Universal Image Segmentation code Syntology ran 0 of 5 samples · 5 unverified Compare
LaRS (8 rows) Mask2Former (Swin-B) LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark code — Compare
S3DIS Area5 (5 rows) SuperCluster Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering code — Compare
ScanNetV2 (5 rows) OneFormer3D OneFormer3D: One Transformer for Unified Point Cloud Segmentation code — Compare
Indian Driving Dataset (4 rows) EfficientPS EfficientPS: Efficient Panoptic Segmentation code — Compare
KITTI Panoptic Segmentation (4 rows) EfficientPS EfficientPS: Efficient Panoptic Segmentation code — Compare
PanNuke (4 rows) LKCell LKCell: Efficient Cell Nuclei Instance Segmentation with Large... code — Compare
ScanNet (4 rows) OneFormer3D OneFormer3D: One Transformer for Unified Point Cloud Segmentation code — Compare
PASTIS (3 rows) Exchanger+Mask2Former Revisiting the Encoding of Satellite Image Time Series code — Compare
COCO panoptic (2 rows) VAN-B6* Visual Attention Network code Syntology ran 0 of 6 samples · 6 unverified Compare
MUSES: MUlti-SEnsor Semantic perception dataset (2 rows) CAFuser (Swin-T) CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic... code Syntology ran 1 of 1 samples · 0 unverified Compare
NYU Depth v2 (2 rows) EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned) PanopticNDT: Efficient and Robust Panoptic Mapping code Syntology ran 0 of 12 samples · 12 unverified Compare
SemanticKITTI (2 rows) P3Former Position-Guided Point Cloud Panoptic Segmentation Transformer code — Compare
ADE20K (1 row) MasQCLIP MasQCLIP for Open-Vocabulary Universal Image Segmentation code — Compare
DALES (1 row) SuperCluster Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering code — Compare
Hypersim (1 row) EMSANet (2x ResNet-34 NBt1D) PanopticNDT: Efficient and Robust Panoptic Mapping code Syntology ran 0 of 12 samples · 12 unverified Compare
KITTI-360 (1 row) SuperCluster Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering code — Compare
Panoptic nuScenes val (1 row) PolarSeg-Panoptic Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic... code — Compare
Panoptic nuScenes test (1 row) (AF)2-S3Net + CenterPoint Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic... code — Compare
PASTIS-R (1 row) Early Fusion Multi-Modal Temporal Attention Models for Crop Mapping from... code — Compare
S3DIS (1 row) SuperCluster Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering code — Compare
SUN-RGBD (1 row) EMSANet Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments code — Compare

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

37 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 37 until expanded.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 257 papers with code (462 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

  • 20 Mar 2017 179 repositories listed Syntology ran 42 of 140 samples · 98 unverified · 23 pointer-only (licence)
    Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
  • 26 May 2020 37 repositories listed Syntology ran 59 of 92 samples · 33 unverified · 19 pointer-only (licence)
    We present a new method that views object detection as a direct set prediction problem.
  • 19 Apr 2020 36 repositories listed Syntology ran 8 of 48 samples · 40 unverified · 23 pointer-only (licence)
    It is well known that featuremap attention and multi-path representation are important for visual recognition.
  • 20 Feb 2022 21 repositories listed Syntology ran 0 of 6 samples · 6 unverified
    In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings.
  • 25 Jun 2021 18 repositories listed
    We hope this work will facilitate state-of-the-art Transformer researches in computer vision.
  • 23 Mar 2020 18 repositories listed Syntology ran 15 of 38 samples · 23 unverified · 24 pointer-only (licence)
    Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.
  • 8 Jan 2019 12 repositories listed Syntology ran 7 of 12 samples · 5 unverified
    In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks.
  • 6 Jun 2022 10 repositories listed Syntology ran 11 of 13 samples · 2 unverified · 13 pointer-only (licence)
    In this paper we present Mask DINO, a unified object detection and segmentation framework.
  • 22 Mar 2022 9 repositories listed Syntology ran 0 of 2 samples · 2 unverified
    For semantic segmentation with UPerNet, FocalNet base at single-scale outperforms Swin by 2.
  • 22 Nov 2019 9 repositories listed Syntology ran 3 of 9 samples · 6 unverified
    In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage…
  • 3 Jan 2018 9 repositories listed
    We propose and study a task we name panoptic segmentation (PS).
  • 21 May 2020 8 repositories listed Syntology ran 1 of 9 samples · 8 unverified · 1 pointer-only (licence)
    Multi-scale inference is commonly used to improve the results of semantic segmentation.
  • 15 Nov 2019 8 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
    We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively.
  • 19 Mar 2018 8 repositories listed Syntology ran 4 of 10 samples · 6 unverified
    We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints.
  • 29 Sep 2022 7 repositories listed
    These models typically employ localized attention mechanisms, such as the sliding-window Neighborhood Attention (NA) or Swin Transformer's Shifted Window Self Attention.
  • 2 Dec 2021 7 repositories listed Syntology ran 2 of 8 samples · 6 unverified
    While only the semantics of each task differ, current research focuses on designing specialized architectures for each task.
  • 15 Dec 2022 6 repositories listed Syntology ran 1 of 4 samples · 3 unverified
    Vision Transformers convert images to sequences by slicing them into patches.
  • 20 Dec 2021 6 repositories listed Syntology ran 2 of 7 samples · 5 unverified
    We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
  • 1 Dec 2020 6 repositories listed
    In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN.
  • 3 Jun 2020 6 repositories listed Syntology ran 0 of 4 samples · 4 unverified
    In this paper, we explore this mechanism in the backbone design for object detection.
  • 17 Mar 2020 5 repositories listed Syntology ran 3 of 11 samples · 8 unverified · 1 pointer-only (licence)
    In this paper, we attempt to remove this constraint by factorizing 2D self-attention into two 1D self-attentions.
  • 3 May 2019 5 repositories listed Syntology ran 1 of 2 samples · 1 unverified
    In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results.
  • 24 Sep 2023 4 repositories listed Syntology ran 0 of 12 samples · 12 unverified
    As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial.
  • 10 Nov 2022 4 repositories listed Syntology ran 0 of 5 samples · 5 unverified
    However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance.
  • 3 Oct 2022 4 repositories listed Syntology ran 4 of 7 samples · 3 unverified
    Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens.
  • 16 Apr 2020 4 repositories listed Syntology ran 2 of 6 samples · 4 unverified
    In this technical report, we present two novel datasets for image scene understanding.
  • 3 Apr 2020 4 repositories listed Syntology ran 0 of 5 samples · 5 unverified
    Instance segmentation is an important task for scene understanding.
  • 12 May 2018 4 repositories listed Syntology ran 0 of 2 samples · 2 unverified
    Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
  • 16 May 2024 3 repositories listed Syntology ran 13 of 14 samples · 1 unverified · 10 pointer-only (licence)
    To facilitate research in this new area, we build a richly annotated PSG-4D dataset consisting of 3K RGB-D videos with a total of 1M frames, each of which is labeled with 4D panoptic segmentation masks as well as…
  • 28 Nov 2023 3 repositories listed Syntology ran 11 of 11 samples · 0 unverified · 3 pointer-only (licence)
    PVSG relates to the existing video scene graph generation (VidSGG) problem, which focuses on temporal interactions between humans and objects grounded with bounding boxes in videos.

Syntology lines on 26 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections