102 datasets found

Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views.

Object counting in images

The dataset used in the paper for unsupervised single image intrinsic decomposition.

The Middlebury dataset is a benchmark for stereo vision and 3D reconstruction.

ImageNet-Sketch is used as target dataset for domain adaptation.

SVHN, MNIST, and MNIST-M are used as source datasets for domain adaptation.

CIFAR-10-C, CIFAR-100-C, and ImageNet-C are used as target datasets for corruption robustness evaluation.

Common corruptions and perturbations are used to evaluate the robustness of neural networks.

Vision Transformer (ViT) is becoming more popular in image processing. We investigate the effectiveness of test-time adaptation (TTA) on ViT, a technique that has emerged to...

Benchmarking neural network robustness to common corruptions and perturbations.

The dataset is used for testing the proposed thermal image super-resolution model.

The KAIST dataset is a collection of video sequences captured in the campus of Korea Advanced Institute of Science and Technology (KAIST). The dataset is used for visual place...

Thermal images, unfazed by extreme lighting conditions unlike visible images, play a pivotal role in applications such as disease diagnosis, military and surveillance,...

Dataset for waste classification using computer vision and deep learning

Semantic segmentation is a crucial and challenging task for image understanding. It aims to predict a dense labeling map for the input image, which assigns each pixel a unique...

The dataset used in the paper is VGGFace2, which is a dataset for face recognition tasks.

dsprites: Disentanglement testing sprites dataset

Instance segmentation (IS) is an important computer vision task, aiming at simultaneously predicting the class label and the binary mask for each instance of interest in an image.

The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...

The dataset used in the paper is the KITTI dataset, which is a benchmark for monocular depth estimation. The dataset consists of a large collection of images and corresponding...