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COCO-Stuff: Thing and stuff classes in context
COCO-Stuff: Thing and stuff classes in context. -
Efficient Semantic Segmentation using Gradual Grouping
Semantic segmentation is a critical computer vision component of autonomous navigation and robotic systems. It involves dense and high dimensional prediction of a label for... -
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA -
ENet: A Deep Neural Network Architecture for Real-time Semantic Segmentation
A dataset for real-time semantic segmentation. -
Semi-supervised Semantic Segmentation with Error Localization Network
Semi-supervised learning of semantic segmentation, which assumes that only a small portion of training images are labeled and the others remain unlabeled. -
KiTS19 Challenge Data
The KiTS19 Challenge Data: a collection of segmented CT imaging and treatment outcomes for 300 patients treated with partial or radical nephrectomy between 2010 and 2018. -
OFFSEG: A Semantic Segmentation Framework For Off-Road Driving
Off-road image semantic segmentation is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures. -
Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation
A method for LiDAR 3D point-cloud semantic segmentation exploiting perceptual information from RGB images and spatial-depth information from point clouds. -
Zurich Summer dataset
The Zurich Summer dataset is a benchmark for semantic segmentation in remote sensing. It contains aerial images with 8 urban classes. -
Boosting LiDAR-based Semantic Labeling
A large-scale automated cross-modal training data generation process for boosting the LiDAR-based semantic labeling performance. -
WeatherProof Dataset
The WeatherProof Dataset is a semantic segmentation dataset with accurate clear and adverse weather image pairs for better consistency loss in training and evaluation. -
FreDSNet dataset
The FreDSNet dataset is a dataset for joint monocular depth estimation and semantic segmentation from single equirectangular panoramas. -
Prototype Refinement Network for Few-Shot Segmentation
Few-shot segmentation targets to segment new classes with few annotated images provided. It is more challenging than traditional semantic segmentation tasks that segment known... -
Context Encoding for Semantic Segmentation
Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous... -
Segment Anything in Medical Images
Segment Anything in medical images. -
Segment Anything
Segment Anything (SAM) model for semantic segmentation of medical images.
