Multi-face Recognition

Guo, Qi; Wang, Zhihui; Fan, Deng-Ping

doi:10.1109/cisp-bmei51763.2020.9263565

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Cross-attention neural networks are developed to effectively capture the relationships between different input data [ 23 , 24 , 25 , 26 , 27 , 28 ], and then an enhanced feature representation can be extracted. Huang et al proposed a novel cross-attention module that collects contextual information from all pixels along its cross-attention paths.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, self-attention and cross-attention have been applied to the proposed intelligent systems [ 25 , 26 ], and the results showed that their method achieved better precision than other approaches. In addition, Lin et al [ 27 ] and Huo et al [ 28 ] use cross-attention mechanisms to enhance multi-scale feature maps in transformer-based neural networks, and then the computational overhead can be greatly reduced. Thus, for a CVD decision system, the cross-attention mechanism is very suitable for clinical application because it reduces computational resources.…”

Section: Introductionmentioning

confidence: 99%

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A Cross-Stage Partial Network and a Cross-Attention-Based Transformer for an Electrocardiogram-Based Cardiovascular Disease Decision System

Lee,

Chuang,

Yeng

et al. 2024

Bioengineering

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Cardiovascular disease (CVD) is one of the leading causes of death globally. Currently, clinical diagnosis of CVD primarily relies on electrocardiograms (ECG), which are relatively easier to identify compared to other diagnostic methods. However, ensuring the accuracy of ECG readings requires specialized training for healthcare professionals. Therefore, developing a CVD diagnostic system based on ECGs can provide preliminary diagnostic results, effectively reducing the workload of healthcare staff and enhancing the accuracy of CVD diagnosis. In this study, a deep neural network with a cross-stage partial network and a cross-attention-based transformer is used to develop an ECG-based CVD decision system. To accurately represent the characteristics of ECG, the cross-stage partial network is employed to extract embedding features. This network can effectively capture and leverage partial information from different stages, enhancing the feature extraction process. To effectively distill the embedding features, a cross-attention-based transformer model, known for its robust scalability that enables it to process data sequences with different lengths and complexities, is employed to extract meaningful embedding features, resulting in more accurate outcomes. The experimental results showed that the challenge scoring metric of the proposed approach is 0.6112, which outperforms others. Therefore, the proposed ECG-based CVD decision system is useful for clinical diagnosis.

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