10 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 1 cited
Separate and Locate: Rethink the Text in Text-based Visual Question Answering
Chengyang Fang, Jiangnan Li, Liang Li +2
Text-based Visual Question Answering (TextVQA) aims at answering questions about the text in images. Most works in this field focus on designing network structures or pre-training…
cs.CV2022★ 10 cited
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering
Chengyang Fang, Gangyan Zeng, Yu Zhou +4
Texts in scene images convey critical information for scene understanding and reasoning. The abilities of reading and reasoning matter for the model in the text-based visual questi…
cs.CL2020★ 1 cited
Learning Better Representation for Tables by Self-Supervised Tasks
Liang Li, Can Ma, Yinliang Yue +2
Table-to-text generation aims at automatically generating natural text to help people to conveniently obtain the important information in tables. Although neural models for table-t…