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Yaxian Wang - IEEE Xplore Author Profile
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Yaxian Wang

Publications
5
Citations
31
Publications by Year
20232024
Co-Authors:
Show All Co-Authors (15)
Image of author Yaxian Wang

Yaxian Wang

Affiliation

School of Computer Science and Technology, Xi’ an Jiaotong University, China
Key Laboratory of Intelligent Networks and Network Security
Ministry of Education
Xi’an, Shaanxi, China

Publication Topics

Biography

Yaxian Wang received the B.E. degree from Hunan Normal University, Changsha, China, in 2019. She is currently pursuing the M.E. degree with the School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China.,Her research interests include visual question answering, interpretable reasoning, and natural language processing.(Based on document published on 28 June 2021).
Publications
5
Citations
31
Publications by Year
20232024
Co-Authors:
Show All Co-Authors (15)
Author's Published Works

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E-GPS: Explainable Geometry Problem Solving via Top-Down Solver and Bottom-Up Generator

Wenjun Wu;Lingling Zhang;Jun Liu;Xi Tang;Yaxian Wang;Shaowei Wang;Qianying Wang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Geometry Problem Solving has drawn growing attention recently due to its application prospects in intelligent ed-ucation field. However, existing methods are still inade-quate to meet the needs of practical application, suffering from the following limitations: 1) explainability is not en-sured which is essential in real teaching scenarios; 2) the small scale and incomplete annotation of existing ...Show More

E-GPS: Explainable Geometry Problem Solving via Top-Down Solver and Bottom-Up Generator

Wenjun Wu;Lingling Zhang;Jun Liu;Xi Tang;Yaxian Wang;Shaowei Wang;Qianying Wang

Year: 2024 | Conference Paper |

DisAVR: Disentangled Adaptive Visual Reasoning Network for Diagram Question Answering

Yaxian Wang;Bifan Wei;Jun Liu;Lingling Zhang;Jiaxin Wang;Qianying Wang

IEEE Transactions on Image Processing
Year: 2023 | Volume: 32 | Journal Article |
Diagram Question Answering (DQA) aims to correctly answer questions about given diagrams, which demands an interplay of good diagram understanding and effective reasoning. However, the same appearance of objects in diagrams can express different semantics. This kind of visual semantic ambiguity problem makes it challenging to represent diagrams sufficiently for better understanding. Moreover, sinc...Show More

DisAVR: Disentangled Adaptive Visual Reasoning Network for Diagram Question Answering

Yaxian Wang;Bifan Wei;Jun Liu;Lingling Zhang;Jiaxin Wang;Qianying Wang

Year: 2023 | Volume: 32 | Journal Article |

Spatial-Semantic Collaborative Graph Network for Textbook Question Answering

Yaxian Wang;Bifan Wei;Jun Liu;Qika Lin;Lingling Zhang;Yaqiang Wu

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2023 | Volume: 33, Issue: 7 | Journal Article |
Cited by: Papers (3)
Textbook Question Answering (TQA) task requires answering questions by reasoning based on both the given diagrams and text context. There are mainly two challenges for the task. First, the diagrams are different from the natural images. Similar shapes or color blocks may express different semantics and there is also a large intra-topic variation for diagrams. Hence, the characteristics of visual s...Show More

Spatial-Semantic Collaborative Graph Network for Textbook Question Answering

Yaxian Wang;Bifan Wei;Jun Liu;Qika Lin;Lingling Zhang;Yaqiang Wu

Year: 2023 | Volume: 33, Issue: 7 | Journal Article |

Multitask Learning for Visual Question Answering

Jie Ma;Jun Liu;Qika Lin;Bei Wu;Yaxian Wang;Yang You

IEEE Transactions on Neural Networks and Learning Systems
Year: 2023 | Volume: 34, Issue: 3 | Journal Article |
Cited by: Papers (21)
Visual question answering (VQA) is a task that machines should provide an accurate natural language answer given an image and a question about the image. Many studies have found that the current VQA methods are heavily driven by the surface correlation or statistical bias in the training data, and lack sufficient image grounding. To address this issue, we devise a novel end-to-end architecture tha...Show More

Multitask Learning for Visual Question Answering

Jie Ma;Jun Liu;Qika Lin;Bei Wu;Yaxian Wang;Yang You

Year: 2023 | Volume: 34, Issue: 3 | Journal Article |

Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering

Jie Ma;Jun Liu;Yaxian Wang;Junjun Li;Tongliang Liu

IEEE Transactions on Neural Networks and Learning Systems
Year: 2023 | Volume: 34, Issue: 1 | Journal Article |
Cited by: Papers (7)
Textbook question answering (TQA) is a task that one should answer non-diagram and diagram questions accurately, given a large context which consists of abundant diagrams and essays. Although lots of studies have made significant progress in the natural image question answering (QA), they are not applicable to comprehending diagrams and reasoning over the long multimodal context. To address the ab...Show More

Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering

Jie Ma;Jun Liu;Yaxian Wang;Junjun Li;Tongliang Liu

Year: 2023 | Volume: 34, Issue: 1 | Journal Article |

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