ADOPD
Ongoing work on understanding and generating multimodal documents
ADOPD is an ongoing research line on document intelligence. It begins with large-scale page decomposition and extends those structured page elements into grounded evidence for multimodal reasoning.
2024
A Large-Scale Document Page Decomposition Dataset
The original ADOPD introduced 120,000 visually diverse document pages spanning more than 1,000 document types, with dense annotations for document structure and semantic page elements.
2026
Thinking with Anchors: Grounded and Efficient Document Reasoning
ADOPD 2026 extends the original page decomposition dataset with human-cleaned captions, fine-grained semantic tags, and reasoning traces grounded in visual anchors such as text boxes and polygon masks. The goal is to move document understanding beyond localization toward evidence-backed multimodal reasoning.
Paper · Hugging Face collection
Data and Model Zoo
- ADOPD2026 contains 120,000 high-resolution document pages with supervision for text detection, semantic region grounding, instance segmentation, page description, and document-level analysis.
- DocCount is a manually curated visual-counting benchmark with 442 document images and explicit semantic counting questions.
- Released models and demos include nine checkpoints across SAM 3/3.1, YOLOv12, RF-DETR, and LocateAnything-3B for detection, segmentation, and grounded document outputs.
Demos
This work is currently under review; more details will be shared once it is published.