- O
VQG (Visual Question Generation)
- opendatalab.com
zipUpdated Sep 21, 2022
Share
Facebook
Twitter
EmailClick to copy linkLink copiedCiteCarnegie Mellon University (2022). VQG (Visual Question Generation) [Dataset]. https://opendatalab.com/OpenDataLab/VQGzip(5229142 bytes)Available download formatsDataset updatedSep 21, 2022Dataset provided byUniversity of Rochester
Carnegie Mellon University
Microsoft ResearchDescriptionVQG is a collection of datasets for visual question generation. VQG questions were collected by crowdsourcing the task on Amazon Mechanical Turk (AMT). The authors provided details on the prompt and the specific instructions for all the crowdsourcing tasks in this paper in the supplementary material. The prompt was successful at capturing nonliteral questions. Images were taken from the MSCOCO dataset.
- w
vqg.in - Historical whois Lookup
- whoisdatacenter.com
csv+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteAllHeart Web Inc, vqg.in - Historical whois Lookup [Dataset]. https://whoisdatacenter.com/domain/vqg.in/csvAvailable download formatsDataset authored and provided byAllHeart Web IncLicensehttps://whoisdatacenter.com/terms-of-use/https://whoisdatacenter.com/terms-of-use/
Time period coveredMar 15, 1985 - Oct 12, 2025DescriptionExplore the historical Whois records related to vqg.in (Domain). Get insights into ownership history and changes over time.
- h
VQG-subset-20k
- huggingface.co
Updated Apr 27, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteSandeep Mishra (2025). VQG-subset-20k [Dataset]. https://huggingface.co/datasets/battleMaster/VQG-subset-20kDataset updatedApr 27, 2025AuthorsSandeep MishraDescriptionbattleMaster/VQG-subset-20k dataset hosted on Hugging Face and contributed by the HF Datasets community
- h
vqg-bangla-images
- huggingface.co
Updated Oct 24, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteBagdad Hossen Jibon (2025). vqg-bangla-images [Dataset]. https://huggingface.co/datasets/bagdad0101/vqg-bangla-imagesDataset updatedOct 24, 2025AuthorsBagdad Hossen JibonDescriptionbagdad0101/vqg-bangla-images dataset hosted on Hugging Face and contributed by the HF Datasets community
- r
VQA1.0
- resodate.org
- service.tib.eu
Updated Nov 25, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteAntol et al. (2024). VQA1.0 [Dataset]. https://resodate.org/resources/aHR0cHM6Ly9zZXJ2aWNlLnRpYi5ldS9sZG1zZXJ2aWNlL2RhdGFzZXQvdnFhMS0wDataset updatedNov 25, 2024Dataset provided byLeibniz Data ManagerAuthorsAntol et al.DescriptionVQA1.0 is a dataset used to derive VQG data, consisting of 82783 training images, 40504 validation images, and 81434 testing images, where each image has 3 associated questions.
ImageCLEFmed_MEDVQA
- kaggle.com
Updated Oct 4, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteDebesh Jha (2023). ImageCLEFmed_MEDVQA [Dataset]. https://www.kaggle.com/datasets/debeshjha1/imageclefmed-medvqaCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedOct 4, 2023AuthorsDebesh JhaDescriptionIdentifying lesions in colonoscopy images is one of medicine's most famous artificial intelligence applications. Until now, the research has focused on single-image or video analysis. With this task, we aim to bring a new aspect to the field by adding multiple modalities to the picture. The task's primary focus will be answering and generating questions. The goal is that through the combination of text and image data, the analysis output gets easier to use by medical experts. The task has three sub-tasks.
For the visual question answering (VQA), the participants must combine images and text answers to answer the questions. In the visual question generation (VQG) subtask, the participants are asked to generate text questions from a given image and answer. Example questions for VQA and VQG: How many polyps are in the image? Are there any polyps in the image? What disease is visible in the image? The third subtask is the visual location question answering (VLQA), where the participants get an image and a question and are required to answer it by providing a segmentation mask for the image. Example questions are: Where in the image is the polyp? Where in the image is the normal and the diseased part? What part of the image shows normal mucosa?
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2024503%2F93cbbf8ced7ce5424e8537b0631d1c99%2FVQA_0.png?generation=1708721778126283&alt=media" alt="">
Not seeing a result you expected?
Learn how you can add new datasets to our index.
FacebookTwitterEmailVQG (Visual Question Generation)
OpenDataLab/VQG
Carnegie Mellon University
Microsoft Research
VQG is a collection of datasets for visual question generation. VQG questions were collected by crowdsourcing the task on Amazon Mechanical Turk (AMT). The authors provided details on the prompt and the specific instructions for all the crowdsourcing tasks in this paper in the supplementary material. The prompt was successful at capturing nonliteral questions. Images were taken from the MSCOCO dataset.