18 datasets found

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

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

The Vaihingen dataset consists of 1440 scenes with a size of 250×250 pixels. Each scene is a colour-infrared (CIR) true orthophoto and a height grid (digital surface model; DSM)...

The Stanford 2D3DS dataset is a dataset of spherical panoramas with corresponding pixelwise semantic labels.

The dataset used for ImageNet recognition, ADE20K semantic segmentation, and COCO panoptic segmentation.

The Synthia dataset is a large-scale urban scene understanding dataset, containing 9000 samples. It is used for semantic segmentation tasks.

Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature...

The dataset used in the paper is GTA5 and SYNTHIA, which are used for domain adaptive semantic segmentation (DASS).

Semantic segmentation is a fundamental task in computer vision and has witnessed great progress using deep learning in the past few years. It aims at segmenting things or stuff...

Radar signal deinterleaving dataset based on semantic segmentation with neural network

Few-shot semantic segmentation via prototype augmentation with image-level annotations

Multi-label image recognition is a practical and challenging task compared to single-label image classification.

The proposed learnable tree filter for structure-preserving feature transform.

A dataset for semantic segmentation of high-resolution remote sensing images.

A benchmark dataset for semantic segmentation of high-resolution remote sensing images.

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...

The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...

The Microsoft COCO dataset was used for training and evaluating the CNNs because it has become a standard benchmark for testing algorithms aimed at scene understanding and...