82 datasets found

The dataset used in the paper is the Shoes dataset, which consists of c.50,000 examples of shoes in RGB color, from 4 different categories and over 3000 different subcategories.

The LAION-2B dataset used in the pre-training of the diffusion model. The dataset consists of 2.17B images, including public and private domains.

COCO Stuff dataset is an extension of the COCO dataset, 164,000 images covering 171 classes are annotated with segmentation masks.

The V-COCO dataset contains 2,533 training images, 2,867 validation images, and 4,946 test images, including 24 action classes.

The LSUN Bedroom and LSUN Cat dataset is a large-scale image dataset used for training and testing the proposed approach.

The AFHQv2 64×64 dataset is another commonly used image dataset for generative models.

The FFHQ 64×64 dataset is a commonly used image dataset for generative models.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors tested their model on various signal reconstruction tasks: 1D sinusoidal...

The FFHQ dataset is a large dataset of high-quality face images with 1024 × 1024 resolution with variations in age, gender, and glasses.

The dataset used in the paper is a multi-graph problem, where each graph represents a different network. The dataset is used to evaluate the performance of the RECS algorithm on...

The dataset used in the paper is the Kodak PhotoCD dataset, which contains twenty-four 768 × 512 images.

The HKU dataset consists of 4447 images with multiple disconnected salient objects or objects touching the image boundary.

Feature matters for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered...

The dataset used in the paper is a wide domain image dataset, and the authors propose a weakly semi-supervised method for disentangling using both labeled and unlabeled data.

The dataset used in the paper is a wide domain image dataset, and the authors propose a weakly semi-supervised method for disentangling using both labeled and unlabeled data.

The dataset used in the paper is a wide domain image dataset, and the authors propose a weakly semi-supervised method for disentangling using both labeled and unlabeled data.

The dataset used in the paper is a wide domain image dataset, and the authors propose a weakly semi-supervised method for disentangling using both labeled and unlabeled data.

The dataset used in the paper is a wide domain image dataset, and the authors propose a weakly semi-supervised method for disentangling using both labeled and unlabeled data.

The dataset used in the paper is a collection of small images, each representing a patch of a jigsaw puzzle. The patches are of the same size and orientation, and the goal is to...

HyperKvasir is a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.