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    Correlation-guided decoding strategy for low-resource Uyghur scene text recognition by Miaomiao Xu, Jiang Zhang, Lianghui Xu, Wushour Silamu, Yanbing Li

    Published 2024-11-01
    “…Specifically, (1) CGDS employs a hybrid encoding strategy that combines Convolutional Neural Network (CNN) and Transformer. This hybrid encoding effectively leverages the advantages of both methods: On one hand, the convolutional properties and shared weight mechanism of CNN allow for efficient extraction of local features, reducing dependency on large datasets and minimizing errors caused by similar characters. …”
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  3. 63

    Skin Lesion Classification Through Test Time Augmentation and Explainable Artificial Intelligence by Loris Cino, Cosimo Distante, Alessandro Martella, Pier Luigi Mazzeo

    Published 2025-01-01
    “…Our findings reveal that Test Time Augmentation enhances the balanced multi-class accuracy of CNN models by up to 0.3%, achieving a balanced accuracy rate of 97.58% on the International Skin Imaging Collaboration (ISIC 2019) dataset. …”
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  4. 64

    Using machine learning-based models for personality recognition by Fatemeh Mohades Deilami, Hossein Sadr, Mozhdeh Nazari

    Published 2021-09-01
    “…Owing to the fact that various filter sizes in CNN may influence its performance, we decided to combine CNN with AdaBoost, a classical ensemble algorithm, to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter size using AdaBoost. …”
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    Explainable analysis of infrared and visible light image fusion based on deep learning by Bo Yuan, Hongyu Sun, YinJing Guo, Qiang Liu, Xinghao Zhan

    Published 2025-01-01
    “…Firstly, a multimodal image fusion model was proposed based on the advantages of convolutional neural networks (CNN) for local context extraction and Transformer global attention mechanism. …”
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    Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition. by Pranjul Gupta, Katharina Dobs

    Published 2025-01-01
    “…Finally, interpretability methods revealed that only a CNN trained for both face identification and object categorization relied on face-like features-such as 'eyes'-to classify pareidolia stimuli as faces, mirroring findings in human perception. …”
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  13. 73

    Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles by Elena Politi, Charalampos Davalas, Christos Chronis, George Dimitrakopoulos, Dimitrios Michail, Iraklis Varlamis

    Published 2025-01-01
    “…Autonomous vehicles rely on sensor data to obtain information of the internal state of the system and the impact of the external environment to achieve self-driving autonomy. …”
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  14. 74

    Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images by Xiaoning Zhang, Yi Yu, Daqun Li, Yuqing Wang

    Published 2025-01-01
    “…Most existing ORSI-SOD methods rely on pre-trained CNN- or Transformer-based backbones to extract features from ORSIs, followed by multi-level feature aggregation. …”
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