8 citations · 8 across the 6 of their papers we have counts for
7 papers · 1 filter
A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends
Yihao Ding, Siwen Luo, Yue Dai +6
Visually Rich Document Understanding (VRDU) has become a pivotal area of research, driven by the need to automatically interpret documents that contain intricate visual, textual, a…
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering
Yihao Ding, Kaixuan Ren, Jiabin Huang +2
Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly those dominated by lengthy textual content like research journal…
PDFVQA: A New Dataset for Real-World VQA on PDF Documents
Yihao Ding, Siwen Luo, Hyunsuk Chung +1
Document-based Visual Question Answering examines the document understanding of document images in conditions of natural language questions. We proposed a new document-based VQA da…
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals
Zhihao Zhang, Siwen Luo, Junyi Chen +4
We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily. The PiggyBack supports the fu…
Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis
Siwen Luo, Yihao Ding, Siqu Long +2
Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications. Recent stud…
VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks
Soyeon Caren Han, Siqu Long, Siwen Luo +2
Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited inform…