58 datasets found

GeneIC uses CUB-200 and Oxford-102 datasets for training and testing.

Fundus image dataset for unsupervised domain adaptive segmentation

Fundus image dataset for unsupervised domain adaptive segmentation

VisDA-C is a dataset for visual domain adaptation, consisting of synthetic rendering of 3D models in the source domain and Microsoft COCO real images in the target domain.

Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to a related but unlabeled target domain.

This paper tackles a novel problem: how to transfer knowledge from the emerging Segment Anything Model (SAM) to learn a compact panoramic semantic segmentation model, i.e.,...

The Amazon review dataset is used for multi-source domain adaptation. It contains review texts and ratings of bought products. Products are grouped into categories. Following...

The dataset used in the paper is a multi-view clustering dataset, which contains 6 views of 30000 samples each. The dataset is used to evaluate the performance of the proposed...

The SYNTHIA dataset is a synthetic dataset for semantic segmentation, and the Cityscapes dataset is a real-world dataset for semantic segmentation.

The OfficeHome dataset is a benchmark for domain generalization, containing 13,000 images from 6 domains: Clipart, Product, Real World, Art, Product, and Real World.

Office-Home, Office-31, and DomainNet are benchmark datasets for semi-supervised domain adaptation.

VisDA-2017 is a simulation-to-real dataset with two extremely distinct domains: Synthetic renderings of 3D models and Real collected from photo-realistic or real-image datasets.

Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain.

The GTA5→Cityscapes dataset is a synthetic-to-real benchmark dataset for domain adaptation in semantic segmentation.

The dataset used in the paper is a cross-domain dataset, consisting of six domains: Real, Painting, Sketch, Clipart, Infograph, and Quickdraw. Each domain contains 345 object...

This dataset is a continual deepfake detection benchmark, which contains images across different domains.

The Visual Domain Decathlon Benchmark consists of 10 image classification tasks that have been explicitly selected to represent different domains.

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...