24 datasets found

The UAVid dataset is used for evaluating the proposed method.

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)...

Dense computer vision tasks such as object detection and segmentation require effective multi-scale feature representation for detecting or classifying objects or regions with...

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

MUAD: A synthetic dataset for autonomous driving with multiple uncertainties and annotations for semantic segmentation, depth estimation, object detection, and instance...

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

The proposed Win Transformer achieves consistently superior performance than Swin Transformer on multiple computer vision tasks, including image recognition, semantic...

The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used the ImageNet-1K, COCO, and ADE20K datasets for image classification,...

The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used CIFAR-10 and CIFAR-100 datasets for image classification, and Stanford...

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.