33 citations · 69 across the 8 of their papers we have counts for
8 papers
An Augmentation Strategy for Visually Rich Documents
Jing Xie, James B. Wendt, Yichao Zhou +2
Many business workflows require extracting important fields from form-like documents (e.g. bank statements, bills of lading, purchase orders, etc.). Recent techniques for automatin…
VRDU: A Benchmark for Visually-rich Document Understanding
Zilong Wang, Yichao Zhou, Wei Wei +2
Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry. Although recen…
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models
Yichao Zhou, James B. Wendt, Navneet Potti +2
A key bottleneck in building automatic extraction models for visually rich documents like invoices is the cost of acquiring the several thousand high-quality labeled documents that…
Simplified DOM Trees for Transferable Attribute Extraction from the Web
Yichao Zhou, Ying Sheng, Nguyen Vo +2
There has been a steady need to precisely extract structured knowledge from the web (i.e. HTML documents). Given a web page, extracting a structured object along with various attri…
FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents
Bill Yuchen Lin, Ying Sheng, Nguyen Vo +1
Extracting structured data from HTML documents is a long-studied problem with a broad range of applications like augmenting knowledge bases, supporting faceted search, and providin…
Active Learning for Skewed Data Sets
Abbas Kazerouni, Qi Zhao, Jing Xie +2
Consider a sequential active learning problem where, at each round, an agent selects a batch of unlabeled data points, queries their labels and updates a binary classifier. While t…