Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing method...
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In this paper, the stochastic space-fractional long-short-wave interaction system (SF-LSWIS) with multiplicative white noise is considered. The stochastic exact solutions including triangular function solutions, hyper...
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Biological vision exhibits exceptional contour perception capabilities. In view of this, research on contour detection guided by biological vision is gradually gaining attention. Inspired by the transmission and proce...
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Graph clustering is a fundamental method for studying complex networks. Some existing approaches focus on the graph data without attributed information. However, graph data in the real world generally have attribute i...
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Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detecti...
Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.
Recently, in order to pursue better detection results, more convolutional layers and deeper networks are a direction pursued by everyone. However, more and more down-sampling convolution or up-sampling operations gene...
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The presence of large-scale heterogeneous IoT devices and AI-based applications has brought significant transmission and computation pressure on the wireless communication system. In this paper, we propose a novel mul...
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With the development of neural networks and the increasing popularity of automatic driving, the calibration of the LiDAR and the camera has attracted more and more attention. This calibration task is multi-modal, wher...
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Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as t...
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Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classificatio...
Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classification and identification, this paper proposes a mushroom classification method based on residual networks. Firstly, a network architecture with multiple residual blocks is designed, and it is trained using an image dataset. Then, a transfer learning strategy is employed to initialize the network parameters from a pre-trained model, followed by fine-tuning to adapt to the mushroom classification task. Finally, multiple testing experiments are conducted to evaluate the effectiveness of the proposed method. The experimental results demonstrate excellent performance of the proposed method in mushroom classification tasks. Compared to traditional feature extraction methods, it can better capture the details and texture features of mushrooms, thereby improving classification accuracy. In conclusion, the mushroom classification method based on residual networks exhibits high accuracy and generalization capability. This method has potential applications in the field of mushroom classification, aiding in the better identification and differentiation of poisonous mushrooms, thereby protecting human health.
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