37 citations · 75 across the 12 of their papers we have counts for
16 papers
DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents
Fuxiao Liu, Hao Tan, Chris Tensmeyer
Vision-language pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-…
Learning the Visualness of Text Using Large Vision-Language Models
Gaurav Verma, Ryan A. Rossi, Christopher Tensmeyer +2
Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and ge…
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding
Zilong Wang, Jiuxiang Gu, Chris Tensmeyer +5
Document images are a ubiquitous source of data where the text is organized in a complex hierarchical structure ranging from fine granularity (e.g., words), medium granularity (e.g…
End-to-end Document Recognition and Understanding with Dessurt
Brian Davis, Bryan Morse, Bryan Price +3
We introduce Dessurt, a relatively simple document understanding transformer capable of being fine-tuned on a greater variety of document tasks than prior methods. It receives a do…
LAFITE: Towards Language-Free Training for Text-to-Image Generation
Yufan Zhou, Ruiyi Zhang, Changyou Chen +6
One of the major challenges in training text-to-image generation models is the need of a large number of high-quality image-text pairs. While image samples are often easily accessi…
Visual FUDGE: Form Understanding via Dynamic Graph Editing
Brian Davis, Bryan Morse, Brian Price +2
We address the problem of form understanding: finding text entities and the relationships/links between them in form images. The proposed FUDGE model formulates this problem on a g…