Panoptic Segmentation
214 papers with code • 24 benchmarks • 32 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 )
Benchmarks
These leaderboards are used to track progress in Panoptic Segmentation
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Libraries
Use these libraries to find Panoptic Segmentation models and implementations
MS COCO
Cityscapes
KITTI
ScanNet
ADE20K
NYUv2
SemanticKITTI
SUN RGB-D
S3DIS
BDD100K
Latest papers
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
wusize/clipself
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However, when transferring the vision-language alignment of CLIP from global image representation to local region representation for the open-vocabulary dense prediction tasks, CLIP ViTs suffer from the domain shift from full images to local image regions.
Mask4Former: Mask Transformer for 4D Panoptic Segmentation
With this intention, we propose Mask4Former for the challenging task of 4D panoptic segmentation of LiDAR point clouds.
Finite Scalar Quantization: VQ-VAE Made Simple
google-research/google-research
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Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets.
ClusterFormer: Clustering As A Universal Visual Learner
clusterformer/clusterformer
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This paper presents CLUSTERFORMER, a universal vision model that is based on the CLUSTERing paradigm with TransFORMER.
Few-Shot Panoptic Segmentation With Foundation Models
robot-learning-freiburg/SPINO
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Concurrently, recent breakthroughs in visual representation learning have sparked a paradigm shift leading to the advent of large foundation models that can be trained with completely unlabeled images.
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase.
Panoptic Vision-Language Feature Fields
In this paper, we propose to the best of our knowledge the first algorithm for open-vocabulary panoptic segmentation in 3D scenes.
Tracking Anything with Decoupled Video Segmentation
hkchengrex/Tracking-Anything-with-DEVA
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ICCV 2023
To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation.
Learning to Upsample by Learning to Sample
tiny-smart/dysample
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ICCV 2023
We present DySample, an ultra-lightweight and effective dynamic upsampler.
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark
The progress in maritime obstacle detection is hindered by the lack of a diverse dataset that adequately captures the complexity of general maritime environments.
