#vision
4 articles
01
·★ MEMBER·PAPER·9 min read
Paper Walkthrough: The Design Fundamentals of Pixel Text Representation Learning
An encoder that reads meaning straight off the pixels, never converting glyphs to character codes. This EMNLP 2026 paper argues that what decides its quality is not data volume but four design choices — explained from zero.
02
·VLMs & Multimodal·★ MEMBER·PAPER·8 min read
Video Understanding from Scratch — From a Pile of Frames to a Sense of Time
A video is not just a lot of images. This piece splits video understanding into three questions — which frames to take (sampling), how to mix them (temporal attention), and how to shrink a long video (compression) — and works through each with equations, interactive figures, and code.
03
·CNNs & Image Recognition·FREE·PAPER·9 min read
The ImageNet Moment — The Day Deep Learning Won
In 2012 an image-recognition contest saw its error rate fall from around 26% to 15% in a single year. Neural networks had existed for three decades — so why then? This is the story of the moment data, compute, and method finally lined up, told down to the technical details.
04
·VLMs & Multimodal·★ MEMBER·PAPER·10 min read
Document AI and OCR Today — How an LLM Ends Up Reading Your Invoices
How machines came to read invoices and scanned PDFs, from first principles: text detection and CTC, how errors are measured (CER), LayoutLM's trick of embedding coordinates alongside words, the OCR-free Donut line, and today's habit of handing the page straight to a VLM — plus the walls that matter in production: tables, handwriting, and hallucination.