117 datasets found

The ImageNet with Adversarial Text Regions (ImageNet-Atr) dataset is a new evaluation set built by adding spotting words to the images of ImageNet evaluation sets.

Diffusion Denoised Smoothing (DDS) requires no new technical ideas on top of what was introduced in the section above.

This dataset is used to evaluate the performance of a Convolutional Neural Network (CNN) on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012).

This dataset is used to evaluate the performance of a Convolutional Neural Network (CNN) on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012).

The proposed attack is based on transfer-based and score-based methods and is both gradient-free and query-efficient.

The dataset used in this paper is ImageNet and CIFAR-10 for image classification, and Cityscapes for semantic segmentation.

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

Convolutional neural networks (CNNs) are ubiquitous in computer vision, with a myriad of effective and efficient variations. Recently, Transformers – originally introduced in...

SqueezeNet is a small CNN that performs well on the ImageNet data set.

AlexNet by Krizhevsky et al. (2012), VGG by Simonyan & Zisserman (2015), GoogleNet (Szegedy et al., 2015) and ResNet (He et al., 2015) have seen state-of-the-art...

The dataset used for object detection on streaming video from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2015.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the ImageNet, Places205, and VOC07 datasets for evaluation.

The dataset used in this paper is a collection of pre-trained convolutional neural networks trained on ImageNet.

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

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the ImageNet dataset and a subset of the CC12M dataset for training.

The ImageNet dataset is a large-scale image database that contains over 14 million images, each labeled with one of 21,841 categories.

The JFT-300M dataset is used for training and evaluation of the proposed Circulant Channel-Specific (CCS) token-mixing MLP.

The ImageNet21K dataset is used for training and evaluation of the proposed Circulant Channel-Specific (CCS) token-mixing MLP.

A dataset of ImageNet images with subtle 3D perspective changes that can break ImageNet-trained classification networks.

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