63 datasets found

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a subset of the validation dataset used for the ImageNet Large Scale Visual...

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

The Pascal Visual Object Classes (VOC) challenge is a benchmark for object detection and segmentation.

A dataset of 3D shapes, consisting of 30,000 images of 6,196 objects.

The paper presents a method for reading digits in natural images using unsupervised feature learning.

The dataset used in the paper is a collection of images from the SVHN dataset, along with labels. The dataset is used for image classification.

The VGG-16 dataset is a large collection of images of objects.

ILSVRC is a large-scale image dataset containing over 1.2 million images across 1,000 classes.

The MNIST database of handwritten digits is a popular benchmark data set for classification algorithms.

Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models.

Object recognition is arguably the most important problem at the heart of computer vision. Recently, Barbu et al. introduced a dataset called ObjectNet which includes objects in...

The dataset used in this paper is a neural network, and the authors used it to test the performance of their lookahead pruning method.

Large-scale vision-and-language models trained on curated and web-scrapped data have led to significant improvements over task-specific models when transferred to downstream...

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...

The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

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.

An image is worth 16x16 words: Transformers for image recognition at scale.

The Microsoft COCO dataset was used for training and evaluating the CNNs because it has become a standard benchmark for testing algorithms aimed at scene understanding and...

A benchmark for low-shot recognition was proposed by Hariharan & Girshick (2017) and consists of a representation learning phase without access to the low-shot classes and a...

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...