Search Results for author: Junjie Xu

Found 8 papers, 3 papers with code

UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images

no code implementations19 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.


DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding

1 code implementation29 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.

Answer Generation Chart Question Answering +5

Learning Graph Filters for Spectral GNNs via Newton Interpolation

no code implementations16 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.

Self-Explainable Graph Neural Networks for Link Prediction

no code implementations21 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.

Link Prediction Node Classification

HP-GMN: Graph Memory Networks for Heterophilous Graphs

1 code implementation15 Oct 2022 Junjie Xu, Enyan Dai, Xiang Zhang, Suhang Wang

Graph neural networks (GNNs) have achieved great success in various graph problems.

Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor

no code implementations28 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.

Class Incremental Learning Image Classification +1

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

no code implementations18 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.

Drug Discovery Fairness

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