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Visual Question Answering (VQA) is a dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. The first version of the dataset was released in October 2015. VQA v2.0 was released in April 2017.

Outside Knowledge Visual Question Answering (OK-VQA) includes more than 14,000 questions that require external knowledge to answer. Source: OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Image Source: https://okvqa.allenai.org/

Visual Question Answering (VQA) v2.0 is a dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. It is the second version of the VQA dataset. - 265,016 images (COCO and abstract scenes) - At least 3 questions (5.4 questions on average) per image - 10 ground truth answers per question - 3 plausible (but likely incorrect) answers per question - Automatic evaluation metric The first version of the dataset was released in October 2015.

VQA-RAD consists of 3,515 question–answer pairs on 315 radiology images.

ST-VQA aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the VQA process. Source: Scene Text Visual Question Answering

The dataset is aimed to perform Visual Question Answering on multipage industry scanned documents. The questions and answers are reused from Single Page DocVQA (SP-DocVQA) dataset. The images also corresponds to the same in original dataset with previous and posterior pages with a limit of up to 20 pages per document.

DocCVQA is a Document Visual Question Answering dataset, where the questions are posed over a whole collection of 14,362 scanned documents. Therefore, the task can be seen as a retrieval-style evidence seeking task where given a question, the aim is to identify and retrieve all the documents in a large document collection that are relevant to answering this question as well as provide the answer.

EVJVQA, the first multilingual Visual Question Answering dataset with three languages: English, Vietnamese, and Japanese, is released in this task. UIT-EVJVQA includes question-answer pairs created by humans on a set of images taken in Vietnam, with the answer created from the input question and the corresponding image. EVJVQA consists of 33,000+ question-answer pairs for evaluating the mQA models.

In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge. The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers. Neural visual question answering models have achieved tremendous growth on large-scale datasets which are mostly for resource-rich languages such as English. However, available datasets narrow the VQA task as the answers selection task or answer classification task. We argue that this form of VQA is far from human ability and eliminates the challenge of the answering aspect in the VQA task by just selecting answers rather than generating them. In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question–answer pairs (QAs).

The RetVQA dataset is a large-scale dataset designed for Retrieval-Based Visual Question Answering (RetVQA). RetVQA is a more challenging task than traditional VQA, as it requires models to retrieve relevant images from a pool of images before answering a question. The need for RetVQA stems from the fact that information needed to answer a question may be spread across multiple images. Here is a detailed summary of the RetVQA dataset: * It is 20 times larger than the closest dataset in this setting, WebQA. * It was derived from the Visual Genome dataset, utilising its questions and annotations of images. * It has 418K unique questions and 16,205 unique precise answers. * The questions are designed to be metadata-independent, meaning they do not rely on information such as captions or tags. * The questions are divided into five categories: * color * shape * count * object-attributes * relation-based. * The dataset includes both binary (yes/no) questions and open-ended questions that require a generative answer. * All answers are free-form and fluent, even for binary questions. For example, a binary question may be "Do the rose and sunflower share the same colour?", and a corresponding answer would be "No, the rose and sunflower do not share the same colour". * Every question in RetVQA requires reasoning over multiple images to arrive at the answer. This contrasts with datasets like WebQA, where a majority of questions can be answered using a single image. * The dataset has, on average, two relevant images and 24.5 irrelevant images per question. This makes it more challenging than datasets like ISVQA, where images are homogeneous and no explicit retrieval is needed.

The MCVQA dataset consists of 248, 349 training questions and 121, 512 validation questions for real images in Hindi and Code-mixed. For each Hindi question, we also provide its 10 corresponding answers in Hindi.

Full-Sentence Visual Question Answering (FSVQA) dataset, consisting of nearly 1 million pairs of questions and full-sentence answers for images, built by applying a number of rule-based natural language processing techniques to original VQA dataset and captions in the MS COCO dataset. Source: The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)