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Joe Barrow

@jbarrow.bsky.social
127 followers 191 following 23 posts

NLP @ Pattern Data Prev: Adobe Research, PhD UMD

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Joe Barrow @jbarrow.bsky.social · 24/09/2025
Now, just because we filtered for the cleanest forms doesn't mean we got _perfect_ forms. There are still a lot of inconsistencies in how people prepare forms! In future work I'll be looking at mitigating data quality issues like these.
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Joe Barrow @jbarrow.bsky.social · 24/09/2025
(Note, this doesn't _just_ apply to Acrobat, it's also better than Apple Preview -- neither Acrobat nor Preview even make an attempt at checkboxes, and they're often fooled by any straight, horizontal line. Left: Acrobat, Right: FFDNet)
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Joe Barrow @jbarrow.bsky.social · 24/09/2025
If we train object detectors to find the form fields on these pages, we get a much cleaner set of forms than if you used Acrobat to automatically prepare your form. (Left: Acrobat, Right: FFDNet).
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Joe Barrow @jbarrow.bsky.social · 24/09/2025
Step 1 is to filter out for the cleanest forms possible. We start with 8MM PDFs from Common Crawl, and work our way down to ~60k of the cleanest forms we can find. The results is a ~500k page dataset, called CommonForms.
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Joe Barrow @jbarrow.bsky.social · 24/09/2025
Paper thread of some work I’m *incredibly* proud of, my first single author paper! Converting a PDF to a fillable form is a hard problem, and a lot of solutions don’t work very well! In CommonForms, I show that you can train models that outperform Adobe Acrobat for <$500! 🧵
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Joe Barrow @jbarrow.bsky.social · 31/05/2025
Pretty sure you want that number to be lower. :p (my stats for DC ridership)
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Joe Barrow @jbarrow.bsky.social · 18/12/2024
Drake meme template.
No to: clear, concise prose
Yes to: negative vspace
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Joe Barrow @jbarrow.bsky.social · 18/12/2024
Gemini 2.0 Flash is pretty good at localization in images. for an LMM (much better than GPT-4o in my experiments).
A picture of a teapot to the right of a teacup, both in a flat-bottomed basket. The teacup has eta and flowers in it. There are 2 blue bounding boxes on the image, one labeled "Teapot" and one labeled "Teacup" that are over the teapot and teacup respectively.
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