StackOverflowVQA: Stack Overflow Visual Question Answering Dataset
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Abstract (arXiv)
In recent years, people have increasingly used AI to help them with their problems by asking questions on different topics. One of these topics can be software-related and programming questions. In this work, we focus on the questions which need the understanding of images in addition to the question itself. We introduce the StackOverflowVQA dataset, which includes questions from StackOverflow that have one or more accompanying images. This is the first VQA dataset that focuses on software-related questions and contains multiple human-generated full-sentence answers. Additionally, we provide a baseline for answering the questions with respect to images in the introduced dataset using the GIT model. All versions of the dataset are available at https://huggingface.co/mirzaei2114.
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References · 7
- 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,516
- 2015VQA: Visual Question Answeringcited by 2,506
- 2022GIT: A Generative Image-to-text Transformer for Vision and Languagecited by 537
- 2021VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Expertscited by 425
- 2022mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionscited by 187
- 2023VQA Therapy: Exploring Answer Differences by Visually Grounding Answerscited by 14
- 2016The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)cited by 7
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