The OCR-VQA dataset is a valuable resource for research in the field of Visual Question Answering (VQA). Let me provide you with some details about it:
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Dataset Overview:
- The OCR-VQA dataset contains a total of 207,572 images along with their associated question-answer pairs.
- These images are related to document content and are accompanied by their corresponding OCR transcriptions¹².
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Purpose and Significance:
- Visual Question Answering (VQA) tasks require models to reason jointly over visual information (such as images) and natural language inputs (such as questions).
- By using this dataset, researchers can develop and evaluate AI models that can effectively understand and answer questions based on visual content and textual context.
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Other Related VQA Datasets:
- Apart from OCR-VQA, there are other VQA datasets available for research and benchmarking:
- ScreenQA: Focused on questions related to screen content.
- MP-DocVQA: A dataset for document-based VQA.
- ChartQA: Specifically designed for answering questions about charts.
- InfographicVQA: For handling questions related to infographics.
Source: Conversation with Bing, 3/15/2024
(1) OCR-VQA Dataset | Papers With Code. https://paperswithcode.com/dataset/ocr-vqa.
(2) GitHub - anisha2102/docvqa: Document Visual Question Answering. https://github.com/anisha2102/docvqa.
(3) VQA: Visual Question Answering. https://visualqa.org/.
(4) allenai/aokvqa: Official repository for the A-OKVQA dataset - GitHub. https://github.com/allenai/aokvqa.