320 datasets found

Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models

The dataset used in the paper is the YFCC-100M dataset, a large-scale image dataset.

The dataset used for image classification tasks, including ImageNet-1K, Food-101, Birds, and Dogs.

Image harmonization is a critical task in computer vision, which aims to adjust the foreground to make it compatible with the background.

The dataset used in this paper for shadow detection, consisting of 4 widely used benchmark datasets: SBU, UCF, ISTD, and CUHK.

This paper uses the ImageNet and COCO2017 validation datasets for testing.

The YCB-Video dataset contains 92 videos of 21 objects with varying textures and sizes under cluttered indoor environments.

The LMO dataset is a subset of the LM dataset consisting of eight objects in more cluttered scenes.

The LM consists of 13 objects with approximately 1.2K images per object. We follow the settings described in [2], which uses 15% of the data for training and the rest for testing.

The dataset used in the paper is Fashion-MNIST, MNIST, SVHN, dSprites, and CIFAR-10.

Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text.

Visual Object Tracking is one of the fundamental tasks in computer vision with applications ranging from human-computer interaction, surveillance, traffic flow monitoring and etc....

The dataset used in this paper is a sliding window ConvNet dataset, which is a collection of 3D images and their corresponding labels.

The dataset used in this paper is a 3D ConvNet dataset, which is a collection of 3D images and their corresponding labels.

The MIT Indoor Scene Recognition dataset contains 67 categories of indoor scenes.

A Full Flow Bidirectional Fusion Network for 6D Pose Estimation from a single RGBD image

The dataset used in the paper is Occluded CIFAR.

The dataset used in the paper is Cluttered MNIST and CIFAR-10.

The ImageNet-32 dataset is a subset of the ImageNet dataset, containing 1,281,167 training samples and 50,000 test samples, distributed across 1,000 labels.

The dataset used in the paper to evaluate the alignment of deep neural networks with human perception.