1 citations · 4 across the 10 of their papers we have counts for
4 papers · 1 filter
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models
Sungnyun Kim, Haofu Liao, Srikar Appalaraju +6
Visual document understanding (VDU) is a challenging task that involves understanding documents across various modalities (text and image) and layouts (forms, tables, etc.). This s…
VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
Ofir Abramovich, Niv Nayman, Sharon Fogel +7
In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language mo…
RAVEN: Multitask Retrieval Augmented Vision-Language Learning
Varun Nagaraj Rao, Siddharth Choudhary, Aditya Deshpande +2
The scaling of large language models to encode all the world's knowledge in model parameters is unsustainable and has exacerbated resource barriers. Retrieval-Augmented Generation…
Enhancing Vision-Language Pre-training with Rich Supervisions
Yuan Gao, Kunyu Shi, Pengkai Zhu +7
We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel pre-training paradigm for Vision-Language Models using data from large-scale web screenshot rendering. U…