Panoptic Segmentation

220 papers with code • 24 benchmarks • 33 datasets

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 )


Use these libraries to find Panoptic Segmentation models and implementations

Most implemented papers

Mask R-CNN

tensorflow/models ICCV 2017

Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.

End-to-End Object Detection with Transformers

facebookresearch/detr ECCV 2020

We present a new method that views object detection as a direct set prediction problem.

ResNeSt: Split-Attention Networks

zhanghang1989/ResNeSt 19 Apr 2020

It is well known that featuremap attention and multi-path representation are important for visual recognition.

SOLOv2: Dynamic and Fast Instance Segmentation

WXinlong/SOLO NeurIPS 2020

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.

Visual Attention Network

Visual-Attention-Network/VAN-Classification 20 Feb 2022

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.

PVT v2: Improved Baselines with Pyramid Vision Transformer

whai362/PVT 25 Jun 2021

We hope this work will facilitate state-of-the-art Transformer researches in computer vision.

Panoptic Feature Pyramid Networks

facebookresearch/detectron2 CVPR 2019

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.

Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

idea-research/maskdino CVPR 2023

In this paper we present Mask DINO, a unified object detection and segmentation framework.

Panoptic Segmentation

cocodataset/panopticapi CVPR 2019

We propose and study a task we name panoptic segmentation (PS).

Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation

bowenc0221/panoptic-deeplab CVPR 2020

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 methods while yielding fast inference speed.