activity
20172021
most citedText and Style Conditioned GAN for Generation of Offline Handwriting Lines

19 citations · 27 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV2021

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…

cs.CV20211 cited

RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks

Kai Li, Curtis Wigington, Chris Tensmeyer +5

Cross-Domain Detection (XDD) aims to train an object detector using labeled image from a source domain but have good performance in the target domain with only unlabeled images. Ex…

cs.HC20211 cited

Lets Make A Story Measuring MR Child Engagement

Duotun Wang, Jennifer Healey, Jing Qian +3

We present the result of a pilot study measuring child engagement with the Lets Make A Story system, a novel mixed reality, MR, collaborative storytelling system designed for grand…

cs.CV202019 cited

Text and Style Conditioned GAN for Generation of Offline Handwriting Lines

Brian Davis, Chris Tensmeyer, Brian Price +3

This paper presents a GAN for generating images of handwritten lines conditioned on arbitrary text and latent style vectors. Unlike prior work, which produce stroke points or singl…

cs.HC20202 cited

Using Behavioral Interactions from a Mobile Device to Classify the Reader's Prior Familiarity and Goal Conditions

Sungjin Nam, Zoya Bylinskii, Christopher Tensmeyer +3

A student reads a textbook to learn a new topic; an attorney leafs through familiar legal documents. Each reader may have a different goal for, and prior knowledge of, their readin…

cs.CV2020

Cross-Domain Document Object Detection: Benchmark Suite and Method

Kai Li, Curtis Wigington, Chris Tensmeyer +6

Decomposing images of document pages into high-level semantic regions (e.g., figures, tables, paragraphs), document object detection (DOD) is fundamental for downstream tasks like…