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Nested Hierarchical Transformer
The dataset used in the paper is not explicitly mentioned, but it is implied to be ImageNet and CIFAR-10/100. -
ImageNet and CIFAR-10 datasets
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used VGG-16, ResNet-50, and MobileNet-v2 models on the ImageNet and CIFAR-10... -
Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects
Synthetic datasets of transparent objects for training NeRF models. -
CAMELYON-17
CAMELYON-17 consists of 145 positive slides and 353 negative slides, where positive patches occupying less than 10% of the tissue area in positive slides. -
Distilled Feature Fields Enable Open-Ended Manipulation
The dataset used in the paper is a collection of RGB images of a tabletop scene, along with their corresponding camera poses and 3D geometry. -
Geometry Sharing Network for 3D Point Cloud Classification and Segmentation
Geometry Sharing Network (GS-Net) for 3D point cloud classification and segmentation -
Liver Lesion Classification
Liver lesion dataset for classification using synthetic data augmentation -
KITTI-360 dataset
The KITTI-360 dataset is an extension of the KITTI dataset, containing 10 new sequences recorded in 2013, with a focus on 360-degree views. -
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
The dataset used in the paper for image inpainting using Denoising Diffusion Probabilistic Models (DDPM). -
A dataset of multi-illumination images in the wild
A dataset of multi-illumination images in the wild. -
CIFAR100 and ImageNet
The dataset used in the paper is CIFAR100 and ImageNet. -
Training dataset generation for bridge game registration
The proposed method of automatic dataset generation for cards detection and classification makes it possible to obtain any number of images of any size, which can be used to... -
Argoverse: 3D tracking and forecasting with rich maps
The Argoverse dataset includes 65 training and 24 validation sequences recorded in Miami and Pittsburgh. -
Hardware-Aware Latency Pruning
The proposed hardware-aware latency pruning (HALP) paradigm. Considering both performance and latency contributions, HALP formulates global structural pruning as a global... -
PERMUTOHEDRAL LATTICE CONVOLUTION
The permutohedral lattice convolution is used to process sparse input features, allowing for efficient filtering of signals that do not lie on a dense grid. -
SqueezeNext dataset
The dataset used in the paper is the SqueezeNext dataset, which is a variant of the SqueezeNext model. -
ResNet18 dataset
The dataset used in the paper is the ResNet18 dataset, which is a convolutional neural network dataset. -
MobileNetV2 dataset
The dataset used in the paper is the MobileNetV2 dataset, which is a pre-trained deep neural network model. -
EfficientNet-Lite0 dataset
The dataset used in the paper is the EfficientNet-Lite0 dataset, which is a variant of the EfficientNet model.
