45 datasets found

Vision models with high overall accuracy often exhibit systematic errors on some important subsets of data, posing potential serious safety concerns. Diagnosing such bugs of...

A dataset for pose estimation and tracking for ASIST system

Three datasets: training, calibration, and evaluation datasets for pedestrian detection task.

A benchmark for stereo matching and depth estimation.

The dataset used for the experiments of the paper "CORE-PERIPHERY PRINCIPLE GUIDED REDISIGN OF SELF-ATTENTION IN TRANSFORMERS"

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used TinyImagenet dataset for pre-training the embedding functions.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, CIFAR-100, and STL-10 datasets for training and testing the...

Aerial IR videos of photovoltaic plants for automatic extraction and georeferencing of PV modules

The MNIST and CIFAR-10 datasets are used to test the theory suggesting the existence of many saddle points in high-dimensional functions.

The authors propose a method for self-supervised representation learning using knowledge distillation and vision transformers.

The authors propose a method for zero-shot classification that leverages conditional density estimates from text-to-image diffusion models.

Diffusion models have recently emerged as the state-of-the-art of generative modeling, demonstrating remarkable results in image synthesis and across other modalities.

Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention. They can be considered a unique instance of self-supervised...

The Moving MNIST dataset consists of videos of MNIST digits.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors analyzed the input and output of each encoder layer in Vision Transformers (VTs)...

The authors used the ImageNet-Compatible and CIFAR-10 datasets for targeted attack experiments.

The dataset used in this paper for classification tasks on spherical-MNIST, atomic energy, Shrec17 data sets, and group testing on diffusion MRI data.

The dataset used in this paper for convolutional neural networks (CNNs) with approximate multiplication.

The dataset used in this work is a custom synthetic dataset generated using the liquid-dsp library, containing 600000 examples of each of 13.8 million examples, with SNRs...

Real world datasets often contain entries with missing elements e.g. in a medical dataset, a patient is unlikely to have taken all possible diagnostic tests.