Scene Segmentation
120 papers with code • 5 benchmarks • 7 datasets
Scene segmentation is the task of splitting a scene into its various object components.
Image adapted from Temporally coherent 4D reconstruction of complex dynamic scenes.
Benchmarks
These leaderboards are used to track progress in Scene Segmentation
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Libraries
Use these libraries to find Scene Segmentation models and implementations
ScanNet
NYUv2
SUN RGB-D
UAVid
StreetHazards
Mila Simulated Floods
Berkeley DeepDrive Video
Most implemented papers
A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
AnyiRao/SceneSeg
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CVPR 2020
Scene, as the crucial unit of storytelling in movies, contains complex activities of actors and their interactions in a physical environment.
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Colorectal cancer (CRC) is the third cause of cancer death worldwide.
End-to-end Learning of Driving Models from Large-scale Video Datasets
gy20073/BDD_Driving_Model
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CVPR 2017
Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or a simulation environment.
Context Prior for Scene Segmentation
ycszen/ContextPrior
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CVPR 2020
Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior.
Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
mobarakol/Surgical_SceneGraph_Generation
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Learning to infer graph representations and performing spatial reasoning in a complex surgical environment can play a vital role in surgical scene understanding in robotic surgery.
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
Domain adaptation is to transfer the shared knowledge learned from the source domain to a new environment, i. e., target domain.
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening
shachoi/RobustNet
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CVPR 2021
Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving.
CondNet: Conditional Classifier for Scene Segmentation
ycszen/CondNet
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The last layer of FCN is typically a global classifier (1x1 convolution) to recognize each pixel to a semantic label.
