1 code implementation • 4 Mar 2024 • Zhanghao Hu, Yijun Yang, Junjie Xu, Yifu Qiu, Pinzhen Chen
Current approaches to question answering rely on pre-trained language models (PLMs) like RoBERTa.
no code implementations • 19 Nov 2023 • Weijie Li, Yitian Wan, Xingjiao Wu, Junjie Xu, Cheng Jin, Liang He
Then, to better utilize image attributes in aesthetic assessment, we propose the Unified Multi-attribute Aesthetic Assessment Framework (UMAAF) to model both absolute and relative attributes of images.
1 code implementation • 29 Oct 2023 • Anran Wu, Luwei Xiao, Xingjiao Wu, Shuwen Yang, Junjie Xu, Zisong Zhuang, Nian Xie, Cheng Jin, Liang He
Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document.
no code implementations • 16 Oct 2023 • Junjie Xu, Enyan Dai, Dongsheng Luo, Xiang Zhang, Suhang Wang
Spectral Graph Neural Networks (GNNs) are gaining attention because they can surpass the limitations of message-passing GNNs by learning spectral filters that capture essential frequency information in graph data through task supervision.
no code implementations • 21 May 2023 • Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang, Junjie Xu, Hui Liu, Suhang Wang
Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc explainers usually adopt another strategy or model to explain a target model, which could misinterpret the target model; and (ii) GNN explainers for node classification identify crucial subgraphs around each node for the explanation; while for link prediction, one needs to explain the prediction for each pair of nodes based on graph structure and node attributes.
1 code implementation • 15 Oct 2022 • Junjie Xu, Enyan Dai, Xiang Zhang, Suhang Wang
Graph neural networks (GNNs) have achieved great success in various graph problems.
no code implementations • 28 Apr 2022 • Yang Yang, Zhiying Cui, Junjie Xu, Changhong Zhong, Wei-Shi Zheng, Ruixuan Wang
In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases.
no code implementations • 18 Apr 2022 • Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, Suhang Wang
Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.
1 code implementation • 18 Sep 2020 • Kwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Minyang Wan, Diego Martinez, Xia Hu
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications.