DiT Annotated Paper

DIT: SELF-SUPERVISED PRE-TRAINING FOR DOCUMENT IMAGE TRANSFORMER

DocumentAI with Images has a new leader in town and its DiT! A yet another stellar paper from the folks at Microsoft advancing the field of DocumentAI. This new paper essentially draws inspiration from various papers to come up with a clean end-to-end pre-trained network for various image tasks like document image classification, document layout analysis, table detection, etc. This also lays a foundation for all the upcoming multimodal networks for document understanding and plays an important role in the upcoming LayoutLMv3. Read along to explore this easy-to-read paper which potentially generates a lot of impact in the field.

Please feel free to read along with the paper with my notes and highlights.

Color	Meaning
Green	Topics about the current paper
Yellow	Topics about other relevant references
Blue	Implementation details/ maths/experiments
Red	Text including my thoughts, questions, and understandings

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CITATION

@misc{https://doi.org/10.48550/arxiv.2203.02378,
  doi = {10.48550/ARXIV.2203.02378},
  url = {https://arxiv.org/abs/2203.02378},
  author = {Li, Junlong and Xu, Yiheng and Lv, Tengchao and Cui, Lei and Zhang, Cha and Wei, Furu},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {DiT: Self-supervised Pre-training for Document Image Transformer},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}