8 datasets found

The VQAv2 dataset, containing open-ended questions on 265k images, with 5.4 questions per image on average.

The VQA dataset is a large-scale visual question answering dataset that consists of pairs of images that require natural language answers.

The dataset used in the paper is a set of sequential vision-and-language tasks, where each task consists of an image and a text input.

The LXMERT dataset is used for visual question answering task. It uses pre-trained weights provided by Tan and Bansal (2019) and fine-tunes it with adaptive approaches mentioned...

The VQA 2.0 dataset is used for visual question answering task. It consists of three sets with a train set containing 83k images and 444k questions, a validation set containing...

The Visual Genome dataset is a large-scale visual question answering dataset, containing 1.5 million images, each with 15-30 annotated entities, attributes, and relationships.

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 COCO-QA dataset is used for visual question answering task. It consists of 123,287 images and 78,736 train and 38,948 test questions.