120 datasets found

The dataset used in the paper is a collection of images of objects with varying viewpoints, used for training and testing the proposed RRB framework.

The ModelNet40 dataset is a collection of 3D models, each with multiple views and annotations.

The Sketchy dataset contains sketches of objects and scenes

AWA2 is an animal dataset containing 37,322 images from 50 classes, with 85 attributes provided by experts to describe the semantic feature of each class.

Interaction Networks: Using a Reinforcement Learner to train other Machine Learning algorithms

The dataset used in this paper for multi-sensor next-best-view planning as matroid-constrained submodular maximization.

The Small-NORB dataset is a dataset of 4,000 images of 6 classes, each class containing 60 images of size 16x16.

The dataset used in the paper is CIFAR10-DVS, a dataset of 10,000 event streams of 128x128 images.

The MiniImagenet dataset is a benchmark for few-shot learning, consisting of 60,000 images from 21 classes, each with 300 images.

The authors propose a novel coordinate transform that normalizes the image coordinates of the events by the timestamp of each event.

The event-based camera is a bio-inspired camera that captures the change of logarithmic light intensity of an image. The authors propose a novel coordinate transform that...

Sketchy is a large collection of sketch-photo pairs. The dataset consists of images from 125 different classes, with 100 photos each.

The PoseCNN dataset is a dataset for 6D object pose estimation in cluttered scenes.

Multi-label image recognition is a practical and challenging task compared to single-label image classification.

The Caltech-256 object category dataset is used for the feature extraction step, and the Omniglot dataset is used for the evaluation.

Shapenet is a large-scale synthesis 3D object dataset, where we follow [9] to use the official test splits of chair, car, and motorbike categories for evaluation since they...

Google Scanned Object dataset contains 3D scanned household items.

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...

Synthetic RGB-D dataset (SynROD) is used for testing the proposed approach in a synthetic-to-real adaptation scenario.

OCID [38] comprises of 96 cluttered scenes, using 89 object instances from ARID, as well as the YCB dataset [7].