Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models-Reference-Cited by-同舟云学术

Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models

Published:2023-07-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

He Jiabang¹^ORCID,Hu Yi¹^ORCID,Wang Lei²^ORCID,Xu Xing¹^ORCID,Liu Ning³^ORCID,Liu Hui⁴^ORCID,Shen Heng Tao⁵^ORCID

Affiliation:

1. University of Electronic Science and Technology of China, Chengdu, China

2. Singapore Management University, Singapore , Singapore

3. Beijing Forestry University, Beijing, China

4. Beijing Rongda Technology Co., Ltd., Beijing, China

5. University of Electronic Science and Technology of China & Peng Cheng Laboratory, Chengdu & Shenzhen, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539618.3591670

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3. Query-driven Generative Network for Document Information Extraction in the Wild

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5. Hao Feng , Wengang Zhou , Jiajun Deng , Yuechen Wang , and Houqiang Li . 2022 . Geometric Representation Learning for Document Image Rectification. In European Conference on Computer Vision. Springer, 475--492 . Hao Feng, Wengang Zhou, Jiajun Deng, Yuechen Wang, and Houqiang Li. 2022. Geometric Representation Learning for Document Image Rectification. In European Conference on Computer Vision. Springer, 475--492.