1 citations · 1 across the 6 of their papers we have counts for
6 papers
Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration
Haoyu Cao, Changcun Bao, Chaohu Liu +6
We propose a novel end-to-end document understanding model called SeRum (SElective Region Understanding Model) for extracting meaningful information from document images, including…
Turning a CLIP Model into a Scene Text Spotter
Wenwen Yu, Yuliang Liu, Xingkui Zhu +3
We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP) model to enhance scene text detection and spotting tasks, transforming it into a robust ba…
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
Wenwen Yu, Chengquan Zhang, Haoyu Cao +24
Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, an…
Panoramic Video Salient Object Detection with Ambisonic Audio Guidance
Xiang Li, Haoyuan Cao, Shijie Zhao +3
Video salient object detection (VSOD), as a fundamental computer vision problem, has been extensively discussed in the last decade. However, all existing works focus on addressing…
GMN: Generative Multi-modal Network for Practical Document Information Extraction
Haoyu Cao, Jiefeng Ma, Antai Guo +5
Document Information Extraction (DIE) has attracted increasing attention due to its various advanced applications in the real world. Although recent literature has already achieved…
Relational Representation Learning in Visually-Rich Documents
Xin Li, Yan Zheng, Yiqing Hu +5
Relational understanding is critical for a number of visually-rich documents (VRDs) understanding tasks. Through multi-modal pre-training, recent studies provide comprehensive cont…