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 with no code
Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians
To address the challenge of the ``black-box" nature of deep learning in medical settings, we combine GCExplainer - an automated concept discovery solution - along with Logic Explained Networks to provide global explanations for Graph Neural Networks.
JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation
Part-aware panoptic segmentation is a problem of computer vision that aims to provide a semantic understanding of the scene at multiple levels of granularity.
Eye vs. AI: Human Gaze and Model Attention in Video Memorability
Understanding the factors that determine video memorability has important applications in areas such as educational technology and advertising.
Self-trained Panoptic Segmentation
Recent advancements in self-supervised learning approaches have shown great potential in leveraging synthetic and unlabelled data to generate pseudo-labels using self-training to improve the performance of instance and semantic segmentation models.
ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor Simulation
We present ASSIST, an object-wise neural radiance field as a panoptic representation for compositional and realistic simulation.
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis
On the highly competitive ADE20K and COCO benchmarks, our data generation method markedly improves the performance of state-of-the-art segmentation models in semantic segmentation, panoptic segmentation, and instance segmentation.
4D-Former: Multimodal 4D Panoptic Segmentation
4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individual objects to be segmented and tracked over time.
Panoptic Out-of-Distribution Segmentation
Deep learning has led to remarkable strides in scene understanding with panoptic segmentation emerging as a key holistic scene interpretation task.
SimPLR: A Simple and Plain Transformer for Scaling-Efficient Object Detection and Segmentation
The ability to detect objects in images at varying scales has played a pivotal role in the design of modern object detectors.
A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition
Our best-performing model achieved a PQ+ score of 81. 33 based on the evaluation metrics of the competition.
