Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Chuanlei Zhang,Wei Chen Conference proceed

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https://doi.org/10.1007/978-3-662-00854-6d on aligning the global distributions of the source and target domains. In this paper, we introduce a subclass domain adaptive network (CASAN) that integrates the Large Margin Cosine Loss and Additive Angular Margin Loss to enhance domain-adaptive classification in scenarios where image quality var

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PHONE

https://doi.org/10.1007/978-3-658-31829-1quired node embeddings to rebuild both the topology and node attributes of the attribute network. Finally, anomaly detection is conducted by evaluating the reconstruction errors of attributes and structures. The experimental results on three attribute network datasets demonstrate the framework’s effectiveness.

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https://doi.org/10.1007/978-3-322-98523-1gression tasks. By integrating these components, our model effectively reduces feature loss in tiny object detection, achieving outstanding results on three aerial datasets: AI-TOD, VisDrone2019, and RSOD. Comparative evaluations against baselines and other detection models demonstrate the superior performance of our approach.

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Grundlagen der Elektrotechnik Iwo parallel convolutional neural network branches. Then, it employs an attention mechanism to weigh the local features of a person, emphasizing regions which are more critical for identification. Finally, the global and weighted local features are fused to obtain the final representation of the person.

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