150 datasets found

COCO-Stuff: Thing and stuff classes in context.

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

A dataset for real-time semantic segmentation.

Semi-supervised learning of semantic segmentation, which assumes that only a small portion of training images are labeled and the others remain unlabeled.

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.

Off-road image semantic segmentation is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures.

IDD dataset for Indian Road Scenarios contains 10K frames from 180 videos.

Object detection dataset used for training and testing the proposed alert system.

The UniSeg dataset is used for multi-dataset semantic segmentation with label shifts.

A method for LiDAR 3D point-cloud semantic segmentation exploiting perceptual information from RGB images and spatial-depth information from point clouds.

The Zurich Summer dataset is a benchmark for semantic segmentation in remote sensing. It contains aerial images with 8 urban classes.

A large-scale automated cross-modal training data generation process for boosting the LiDAR-based semantic labeling performance.

The WeatherProof Dataset is a semantic segmentation dataset with accurate clear and adverse weather image pairs for better consistency loss in training and evaluation.

The DeepLab dataset is a benchmark for semantic segmentation.

The FreDSNet dataset is a dataset for joint monocular depth estimation and semantic segmentation from single equirectangular panoramas.

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...

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 (SAM) model for semantic segmentation of medical images.