33 citations · 123 across the 26 of their papers we have counts for
7 papers · 1 filter
Distilling Object Detectors With Global Knowledge
Sanli Tang, Zhongyu Zhang, Zhanzhan Cheng +4
Knowledge distillation learns a lightweight student model that mimics a cumbersome teacher. Existing methods regard the knowledge as the feature of each instance or their relations…
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting
Ying Chen, Liang Qiao, Zhanzhan Cheng +3
End-to-end text spotting has attached great attention recently due to its benefits on global optimization and high maintainability for real applications. However, the input scale h…
E2-AEN: End-to-End Incremental Learning with Adaptively Expandable Network
Guimei Cao, Zhanzhan Cheng, Yunlu Xu +4
Expandable networks have demonstrated their advantages in dealing with catastrophic forgetting problem in incremental learning. Considering that different tasks may need different…
TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents
Zhanzhan Cheng, Peng Zhang, Can Li +6
Recently, automatically extracting information from visually rich documents (e.g., tickets and resumes) has become a hot and vital research topic due to its widespread commercial v…
DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding
Liang Qiao, Hui Jiang, Ying Chen +9
This paper presents DavarOCR, an open-source toolbox for OCR and document understanding tasks. DavarOCR currently implements 19 advanced algorithms, covering 9 different task forms…
PMAL: Open Set Recognition via Robust Prototype Mining
Jing Lu, Yunxu Xu, Hao Li +2
Open Set Recognition (OSR) has been an emerging topic. Besides recognizing predefined classes, the system needs to reject the unknowns. Prototype learning is a potential manner to…
Technical Report for ICCV 2021 Challenge SSLAD-Track3B: Transformers Are Better Continual Learners
Duo Li, Guimei Cao, Yunlu Xu +2
In the SSLAD-Track 3B challenge on continual learning, we propose the method of COntinual Learning with Transformer (COLT). We find that transformers suffer less from catastrophic…