12 datasets found

The dataset used in the paper is the Caltech 256 database.

Fine-Grained Visual Classification (FGVC) is an important computer vision prob-lem that involves small diversity within the different classes, and often requires expert...

The dataset used in the paper is a set of images, and the authors used it to train and test their ladder network model.

The dataset used in the paper Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects. The dataset consists of 30 unique 3D object models...

Stanford Dogs, Stanford Cars, Oxford 102 Flowers

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen...

The VoxNet dataset is a 3D object recognition dataset.

The Stanford dataset consists of a large-scale collection of aerial images and videos of a university campus containing various agents (cars, buses, bicycles, golf carts,...

Common Objects in 3D: Large-scale learning and evaluation of real-life 3D category reconstruction

Google Scanned Objects is a real-scanned 3D object dataset and we use all its 1030 samples for evaluation unlike existing works [29, 31] that only use 30 of them.

A large-scale dataset of 3D objects for training and testing 3D reconstruction models.

Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose...