Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and...
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This study is dedicated to solving the problem of single-image super-resolution reconstruction, particularly by introducing a multi-scale attention mechanism to enhance the reconstruction effectiveness. Advances in su...
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Examination and evaluation are effective methods for assessing the effectiveness of teaching and the quality of talent cultivation, which are essential components of the teaching process. Traditional course assessment...
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With the advancement of deep learning and the flourishing of the economy and finance, intelligent finance has received high attention. Traditional time series models and machine learning models are no longer able to m...
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Semantic segmentation in aquatic scenes is key technology water environment monitoring. Small-scale object detection and segmentation in aquatic scenes are major challenges in semantic segmentation of water bodies. Cu...
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ISBN:
(数字)9781510674967
ISBN:
(纸本)9781510674950
Semantic segmentation in aquatic scenes is key technology water environment monitoring. Small-scale object detection and segmentation in aquatic scenes are major challenges in semantic segmentation of water bodies. Current typical semantic segmentation methods often use multi-scale feature fusion operations, features of different scales from different network layers are aggregated, enabling the features to have both strong semantic representation from high-level features and strong feature detail expression capability from low-level features. However, current methods, although they focus on the details of small-scale objects, primarily rely on low-level features to determine the presence of objects in the network scale adaptation for small object detection, resulting in the loss of accuracy when using high-level semantic features for prediction. Moreover, cross-scale fusion does not depend on category characteristics. Therefore, existing methods are not ideal for semantic-constrained small object segmentation, such as water surface garbage and plant debris. Our method focuses on the cross-level semantic information aggregation and utilization for object segmentation in aquatic scenes, providing a new approach for small object segmentation in complex semantic environments. In aquatic scenes, the category of objects has strong contextual relevance. Therefore, this paper proposes a cross-level semantic aggregation network to address the problem of small object segmentation in aquatic scenes. The cross-level semantic aggregation method guides the high-level features to perform semantic aggregation using low-level features, enabling the aggregation of features with high-level semantic features of the same category as small objects, while introducing relevant contextual scene features of different categories. Compared to traditional scale fusion, this introduces a new aggregation method within the semantic framework to handle small object segmentation in complex contextua
Rolling bearing fault diagnosis is an important task that is critical for realizing predictive maintenance in industry 4.0. However, due to the lack of labeled data of rolling bearing faults and the complexity of work...
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Searching for code aims to return code snippets that correspond to specified queries. Improving the accuracy of matching between heterogeneous natural language query inputs and highly structured program language sourc...
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Quantum machine learning has emerged as a promising frontier, leveraging the principles of quantum computing to enhance classical algorithms. This paper proposes an optimized Quantum Support Vector Classifier (QSVC) f...
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Recently, neural architecture search (NAS) methods have demonstrated remarkable results in hyperspectral image (HSI) classification. However, deep network structures often require consideration of model overfitting, a...
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Graph Convolutional Network (GCN) has shown great potential in graph learning. However, since the message-passing mechanism treats all messages equally and updates representation of each node by transforming messages ...
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