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Mingzhe Chen

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3 papers

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AAAI Conference 2026 Conference Paper

Fair Graph Learning with Limited Sensitive Attribute Information

Zichong Wang
Jie Yang
Jun Zhuang
Puqing Jiang
Mingzhe Chen
Ye Hu
Wenbin Zhang

Graph neural networks (GNNs) excel at modeling graph-structured data but often inherit and amplify biases, leading to substantial efforts in developing fair GNNs. However, most existing approaches assume full access to sensitive attribute information, which is often impractical in real-world scenarios due to privacy concerns or risks of discrimination. To address this limitation, this paper focuses on graph fairness with limited sensitive attribute information, ensuring applicability to real-world contexts where current methods fall short. Specifically, we introduce an innovative fairness optimization strategy, propose a novel framework named FGLISA, and provide a theoretical perspective linking limited sensitive attribute information access to fairness objectives, thus enabling fair graph learning in real-world applications with limited sensitive attribute information. Experiments on diverse real-world datasets and tasks validate the effectiveness of our approach in achieving both fairness and predictive performance.

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AAAI Conference 2024 Conference Paper

Cross-Modal Feature Distribution Calibration for Few-Shot Visual Question Answering

Jing Zhang
Xiaoqiang Liu
Mingzhe Chen
Zhe Wang

Few-shot Visual Question Answering (VQA) realizes few-shot cross-modal learning, which is an emerging and challenging task in computer vision. Currently, most of the few-shot VQA methods are confined to simply extending few-shot classification methods to cross-modal tasks while ignoring the spatial distribution properties of multimodal features and cross-modal information interaction. To address this problem, we propose a novel Cross-modal feature Distribution Calibration Inference Network (CDCIN) in this paper, where a new concept named visual information entropy is proposed to realize multimodal features distribution calibration by cross-modal information interaction for more effective few-shot VQA. Visual information entropy is a statistical variable that represents the spatial distribution of visual features guided by the question, which is aligned before and after the reasoning process to mitigate redundant information and improve multi-modal features by our proposed visual information entropy calibration module. To further enhance the inference ability of cross-modal features, we additionally propose a novel pre-training method, where the reasoning sub-network of CDCIN is pretrained on the base class in a VQA classification paradigm and fine-tuned on the few-shot VQA datasets. Extensive experiments demonstrate that our proposed CDCIN achieves excellent performance on few-shot VQA and outperforms state-of-the-art methods on three widely used benchmark datasets.

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AAAI Conference 2021 Conference Paper

DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation

Zhicong Yan
Gaolei Li
Yuan Tian
Jun Wu
Shenghong Li
Mingzhe Chen
H. Vincent Poor

The threat of data-poisoning backdoor attacks on learning algorithms typically comes from the labeled data used for learning. However, in deep semi-supervised learning (SSL), unknown threats mainly stem from unlabeled data. In this paper, we propose a novel deep hidden backdoor (DeHiB) attack for SSL-based systems. In contrast to the conventional attacking methods, the DeHiB can feed malicious unlabeled training data to the semi-supervised learner so as to enable the SSL model to output premeditated results. In particular, a robust adversarial perturbation generator regularized by a unified objective function is proposed to generate poisoned data. To alleviate the negative impact of trigger patterns on model accuracy and improve the attack success rate, a novel contrastive data poisoning strategy is designed. Using the proposed data poisoning scheme, one can implant the backdoor into the SSL model using the raw data without handcrafted labels. Extensive experiments based on CIFAR10 and CIFAR100 datasets demonstrates the effectiveness and crypticity of the proposed scheme.

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