11 datasets found

A large-scale benchmark archive for remote sensing image classification and retrieval.

MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval.

The Stanford Online Products (SOP) dataset contains 120,053 product images covering 22,634 categories. The training set is composed of 59,551 images of the first 11,318...

The dataset used in the paper is a synthetic dataset for blind super-resolution, consisting of low-resolution images and their corresponding blur kernels.

The dataset used in the paper is a multi-graph problem, where each graph represents a different network. The dataset is used to evaluate the performance of the RECS algorithm on...

The dataset used in the paper is a multi-view clustering dataset, which contains 6 views of 30000 samples each. The dataset is used to evaluate the performance of the proposed...

The LabelMe dataset is a natural scene dataset used for testing the performance of the IBTM model on image classification tasks.

The dataset used in the paper is YFCC100M, a large-scale video dataset. The dataset is used for foreground and background patch extraction and object recognition tasks.

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...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used it for language modeling and image classification tasks.

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...