The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
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- Type
- article
- Published
- 2016-06-27
- Cited by
- 2,478
- References
- 43
- OpenAlex
- https://openalex.org/W2431874326
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:206594095
Keywords
Computer science, Segmentation, Artificial intelligence, Convolutional neural network, Pixel
References
- SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Learning Deconvolution Network for Semantic Segmentation
- Fully convolutional networks for semantic segmentation
- Learning scene-specific pedestrian detectors without real data
- Return of the Devil in the Details: Delving Deep into Convolutional Nets
- Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models
- Large-Scale Video Classification with Convolutional Neural Networks
- Unbiased look at dataset bias
- Virtual and Real World Adaptation for Pedestrian Detection
- Evaluation of image features using a photorealistic virtual world
- Domain Adaptation of Deformable Part-Based Models
- Real-time human pose recognition in parts from single depth images
- Vision-Based Offline-Online Perception Paradigm for Autonomous Driving
- Articulated people detection and pose estimation: Reshaping the future
- From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- LabelMe: A Database and Web-Based Tool for Image Annotation
- Vision meets robotics: The KITTI dataset
Cited by
- Play and Learn: Using Video Games to Train Computer Vision Models
- Generating Synthetic Data for Text Recognition
- 3D Simulation for Robot Arm Control with Deep Q-Learning
- Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?
- GPU-Accelerated Real-Time Stixel Computation
- Convolutional gated recurrent networks for video segmentation
- End-to-End learning of cost-volume aggregation for real-time dense stereo
- RenderGAN: Generating Realistic Labeled Data
- Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
- TorontoCity: Seeing the World with a Million Eyes
- The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
- Procedural Generation of Videos to Train Deep Action Recognition Networks
- Kangaroo Vehicle Collision Detection Using Deep Semantic Segmentation Convolutional Neural Network
- FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
- Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks
- Learning from Simulated and Unsupervised Images through Adversarial Training
- Adversarially Tuned Scene Generation
- SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth
- UnrealStereo: A Synthetic Dataset for Analyzing Stereo Vision
- Domain Adaptation for Visual Applications: A Comprehensive Survey
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