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Chong Fu

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5 papers

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EAAI Journal 2026 Journal Article

Quantum-driven neural network with masked self-attention for multi-modal driving fatigue detection

Xu Xu
Chong Fu

Driving fatigue detection is crucial for improving safety in intelligent autonomous transport systems. Recently, many studies have developed multi-modal algorithms for this purpose. However, two challenges remain unsolved: high uncertainty in signals and low robustness in model detection. To address these issues, we develop a quantum-driven framework called Q-Fatigue for multi-modal driving fatigue detection using physiological signals. It is based on masked self-attention and a quantum circuit layer. Masked self-attention helps the model focus on the most informative features and ignore irrelevant parts. The quantum circuit layer acts as a compact, nonlinear transformation within multi-modal physiological signals, improving feature interaction with fewer parameters. Q-Fatigue is trained and tested on the seed vigilance electroencephalogram (SEED-VIG) and sustained-attention driving task (SADT) datasets under a cross-subject setting. Experimental results show that it is effective and outperforms existing methods.

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JBHI Journal 2025 Journal Article

Multi-Scale Dynamic Sparse Attention UNet for Medical Image Segmentation

Xiang Li
Chong Fu
Qun Wang
Wenchao Zhang
Chen Ye
Junxin Chen
Chiu-Wing Sham

Transformers have recently gained significant attention in medical image segmentation due to their ability to capture long-range dependencies. However, the presence of excessive background noise in large regions of medical images introduces distractions and increases the computational burden on the fine-grained self-attention (SA) mechanism, which is a key component of the transformer model. Meanwhile, preserving fine-grained details is essential for accurately segmenting complex, blurred medical images with diverse shapes and sizes. Thus, we propose a novel Multi-scale Dynamic Sparse Attention (MDSA) module, which flexibly reduces computational costs while maintaining multi-scale fine-grained interactions with content awareness. Specifically, multi-scale aggregation is first applied to the feature maps to enrich the diversity of interaction information. Then, for each query, irrelevant key-value pairs are filtered out at a coarse-grained level. Finally, fine-grained SA is performed on the remaining key-value pairs. In addition, we design an enhanced downsampling merging (EDM) module and an enhanced upsampling fusion (EUF) module for building pyramid architectures. Using MDSA to construct the basic blocks, combined with EDMs and EUFs, we develop a UNet-like model named MDSA-UNet. Since MDSA-UNet dynamically processes only a small subset of relevant fine-grained features, it achieves strong segmentation performance with high computational efficiency. Extensive experiments on four datasets spanning three different types demonstrate that our MDSA-UNet, without using pre-training, significantly outperforms other non-pretrained methods and even competes with pre-trained models, achieving Dice scores of 82. 10% on DDTI, 80. 20% on TN3K, 90. 75% on ISIC2018, and 91. 05% on ACDC. Meanwhile, our model maintains lower complexity, with only 6. 65 M parameters and 4. 54 G FLOPs at a resolution of 224 × 224, ensuring both effectiveness and efficiency. Code is available at URL.

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AAAI Conference 2023 Conference Paper

Boosting Semi-Supervised Semantic Segmentation with Probabilistic Representations

Haoyu Xie
Changqi Wang
Mingkai Zheng
Minjing Dong
Shan You
Chong Fu
Chang Xu

Recent breakthroughs in semi-supervised semantic segmentation have been developed through contrastive learning. In prevalent pixel-wise contrastive learning solutions, the model maps pixels to deterministic representations and regularizes them in the latent space. However, there exist inaccurate pseudo-labels which map the ambiguous representations of pixels to the wrong classes due to the limited cognitive ability of the model. In this paper, we define pixel-wise representations from a new perspective of probability theory and propose a Probabilistic Representation Contrastive Learning (PRCL) framework that improves representation quality by taking its probability into consideration. Through modelling the mapping from pixels to representations as the probability via multivariate Gaussian distributions, we can tune the contribution of the ambiguous representations to tolerate the risk of inaccurate pseudo-labels. Furthermore, we define prototypes in the form of distributions, which indicates the confidence of a class, while the point prototype cannot. More- over, we propose to regularize the distribution variance to enhance the reliability of representations. Taking advantage of these benefits, high-quality feature representations can be derived in the latent space, thereby the performance of se- mantic segmentation can be further improved. We conduct sufficient experiment to evaluate PRCL on Pascal VOC and CityScapes to demonstrate its superiority. The code is available at https://github.com/Haoyu-Xie/PRCL.

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JBHI Journal 2022 Journal Article

Low-Cost and Confidential ECG Acquisition Framework Using Compressed Sensing and Chaotic Systems for Wireless Body Area Network

Hui Zhang
Junxin Chen
Leo Yu Zhang
Chong Fu
Raffaele Gravina
Giancarlo Fortino
Zhihan Lv

Recent years have witnessed an increasing popularity of wireless body area network (WBAN), with which continuous collection of physiological signals can be conveniently performed for healthcare monitoring. Energy consumption is a critical issue because it directly affects the duration of the equipped sensors. In this article, we propose a low-cost and confidential electrocardiogram (ECG) acquisition approach for WBAN. The compressed sensing (CS) is employed for low-cost signal acquisition, and its cryptographic features are exploited for promoting the framework's confidentiality. In particular, the RIPless measurement matrix is used to give CS the resistance against plaintext attack, while the first-order $\Sigma \Delta$ quantizer is employed to embed the cryptographic diffusion feature into the whole system. Two chaotic systems are employed for generating the required secret elements for the acquisition and encryption. Experiment results well demonstrate the signal reconstruction and security performance of the proposed framework.

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NeurIPS Conference 2022 Conference Paper

Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models

Biru Zhu
Yujia Qin
Ganqu Cui
Yangyi Chen
Weilin Zhao
Chong Fu
Yangdong Deng
Zhiyuan Liu

Despite the great success of pre-trained language models (PLMs) in a large set of natural language processing (NLP) tasks, there has been a growing concern about their security in real-world applications. Backdoor attack, which poisons a small number of training samples by inserting backdoor triggers, is a typical threat to security. Trained on the poisoned dataset, a victim model would perform normally on benign samples but predict the attacker-chosen label on samples containing pre-defined triggers. The vulnerability of PLMs under backdoor attacks has been proved with increasing evidence in the literature. In this paper, we present several simple yet effective training strategies that could effectively defend against such attacks. To the best of our knowledge, this is the first work to explore the possibility of backdoor-free adaptation for PLMs. Our motivation is based on the observation that, when trained on the poisoned dataset, the PLM's adaptation follows a strict order of two stages: (1) a moderate-fitting stage, where the model mainly learns the major features corresponding to the original task instead of subsidiary features of backdoor triggers, and (2) an overfitting stage, where both features are learned adequately. Therefore, if we could properly restrict the PLM's adaptation to the moderate-fitting stage, the model would neglect the backdoor triggers but still achieve satisfying performance on the original task. To this end, we design three methods to defend against backdoor attacks by reducing the model capacity, training epochs, and learning rate, respectively. Experimental results demonstrate the effectiveness of our methods in defending against several representative NLP backdoor attacks. We also perform visualization-based analysis to attain a deeper understanding of how the model learns different features, and explore the effect of the poisoning ratio. Finally, we explore whether our methods could defend against backdoor attacks for the pre-trained CV model. The codes are publicly available at https: //github. com/thunlp/Moderate-fitting.

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