992 datasets found

The dataset used in the paper is YFCC100M, a large-scale video dataset. The dataset is used for foreground and background patch extraction and object recognition tasks.

The DTU MVS Dataset and the Local Light Field Fusion Dataset are used to evaluate the performance of the proposed GARF model.

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

The dataset for 2D triangle packing, 2D shape arrangement with qualitative constraints, 3D object stacking with stability constraints, and 3D object packing with robots.

The dataset used in this paper is the Meta Research's Segment Anything Model (SAM) dataset, which consists of images.

RotNIST dataset

The ScanNet dataset is a large-scale indoor dataset composed of monocular sequences with ground truth poses and depth images.

Dense pixel matching is required for many computer vision algorithms such as disparity, optical flow or scene flow estimation. Feature Pyramid Networks (FPN) have proven to be a...

The dataset used in the paper is CIFAR-10, STL-10, and ImageNet.

The dataset used in the paper is the KITTI Vision Benchmark and Cityscapes dataset for monocular depth estimation and structure prediction.

The dataset used in this paper is a ResNet50 model, which is a convolutional neural network. The model is used to evaluate the effects of quantization on the compute...

A real-world dataset for motion-blurred hand extraction task.

A synthetic dataset for motion-blurred hand extraction task.

The dataset used in the paper is CIFAR-10 and ILSVRC-2012.

The dataset used in this paper is ResNet50, ResNet34, and ResNet18.

The dataset used in this paper is VGG19-BN, CIFAR-10, and CIFAR-100.

A point cloud dataset with large quantity of dynamic instances, consisting of 2,988 real-world scans with point-level annotations.

The Lung Image Database Consortium (LIDC) dataset contains 1609 2D thoracic CT scans belonging to 214 subjects.

MobileNet is a compact network working accurately and efficiently based on light-weight module of separable convolution layers.

Single-image human relighting aims to relight a target human under new lighting conditions by decomposing the input image into albedo, shape and lighting.