Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset
On the map: Multimodal LLMs / vision-language models → Chart understanding and anomaly detection
Abstract (arXiv)
Using vision-language models (VLMs) in web development presents a promising strategy to increase efficiency and unblock no-code solutions: by providing a screenshot or a sketch of a UI, a VLM could generate the code to reproduce it, for instance in a language like HTML. Despite the advancements in VLMs for various tasks, the specific challenge of converting a screenshot into a corresponding HTML has been minimally explored. We posit that this is mainly due to the absence of a suitable, high-quality dataset. This work introduces WebSight, a synthetic dataset consisting of 2 million pairs of HTML codes and their corresponding screenshots. We fine-tune a foundational VLM on our dataset and show proficiency in converting webpage screenshots to functional HTML code. To accelerate the research in this area, we open-source WebSight.
Key facts. Cited by 126 papers in the map (data through 7 Oct 2026). Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 5 of its 11 references that are in the map. First posted on arXiv 14 Mar 2024.
Builds on
5 of 11 references.
References · 5
- 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,154
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- 2023Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Textcited by 208
- 2017pix2code: Generating Code from a Graphical User Interface Screenshotcited by 113
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Citing papers per year: 2024: 24; 2025: 49; 2026: 53.
126 papers.
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