@inproceedings{939b0209ee6640b6b114433cf495d71c,
title = "Element Information Enhancement for Diagram Question Answering with Synthetic Data",
abstract = "Unlike natural pictures, diagrams are a highly abstract vehicle for knowledge representation, and Diagram Question Answering involves complex reasoning processes such as diagram element detection. However, due to low resource constraints, achieving efficient extraction of diagram elements is challenging. In addition, vision tasks rely on image feature extraction, and most feature extraction today is based on real scenario images on ImageNet. To solve the above problems, we programmatically synthesized a diagram dataset to implement diagram element prediction and put its feature extraction module to use on downstream task. In the actual task, we explicitly input the predicted image elements from the diagram into the model. The experimental comparison shows a significant improvement in our model compared to the baseline.",
keywords = "Data synthesis, Diagram question answering, Low resource",
author = "Yadong Zhang and Yang Chen and Yupei Ren and Man Lan and Yuefeng Chen",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 7th China Conference on Knowledge Graph and Semantic Computing Evaluations, CCKS 2022 ; Conference date: 24-08-2022 Through 27-08-2022",
year = "2022",
doi = "10.1007/978-981-19-8300-9\_9",
language = "英语",
isbn = "9789811982996",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "78--86",
editor = "Ningyu Zhang and Meng Wang and Tianxing Wu and Wei Hu and Shumin Deng",
booktitle = "CCKS 2022 - Evaluation Track - 7th China Conference on Knowledge Graph and Semantic Computing Evaluations, CCKS 2022, Revised Selected Papers",
address = "德国",
}