Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection

Xiang, Junjiang; Pan, Qing; Zhang, Zhengrong; Fu, Songnian; Qin, Yuwen

doi:10.1007/s11432-022-3592-8

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Attention mechanisms 19,20 have become a widely used technique in various sequence‐based tasks. One of the main advantages of attention mechanisms is the ability to handle inputs of variable sizes by focusing on the most relevant parts of the input to make informed decisions.…”

Section: Gat Architecture and Federated Learningmentioning

confidence: 99%

Greedy‐based user selection for federated graph neural networks with limited communication resources

Huangfu,

Zhang

2024

Computational Intelligence

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Recently, graph neural networks (GNNs) have attracted much attention in the field of machine learning due to their remarkable success in learning from graph‐structured data. However, implementing GNNs in practice faces a critical bottleneck from the high complexity of communication and computation, which arises from the frequent exchange of graphic data during model training, especially in limited communication scenarios. To address this issue, we propose a novel framework of federated graph neural networks, where multiple mobile users collaboratively train the global model of graph neural networks in a federated way. The utilization of federated learning into the training of graph neural networks can help reduce the communication overhead of the system and protect the data privacy of local users. In addition, the federated training can help reduce the system computational complexity significantly. We further introduce a greedy‐based user selection for the federated graph neural networks, where the wireless bandwidth is dynamically allocated among users to encourage more users to attend the federated training of neural networks. We perform the convergence analysis on the federated training of neural networks, in order to obtain some more insights on the impact of critical parameters on the system design. Finally, we perform the simulations on the coriolis ocean for reAnalysis (CORA) dataset and show the advantages of the proposed method in this paper.

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Section: Gat Architecture and Federated Learningmentioning

confidence: 99%

Greedy‐based user selection for federated graph neural networks with limited communication resources

Huangfu,

Zhang

2024

Computational Intelligence

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“…For example, in image classification tasks, channel attention can help the network to better distinguish feature differences between different categories [ 13 , 14 , 15 , 16 , 17 ]. In a target detection task, channel attention improves the network’s ability to accurately locate and recognize targets [ 18 , 19 , 20 , 21 , 22 ]. However, current channel attention modules simply and crudely use global pooling in the compression process, which may lose locally important information and lead to relatively limited modeling of complex semantic relationships.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in image classification tasks, channel attention can help the network to better distinguish feature differences between different categories [13][14][15][16][17]. In a target detection task, channel attention improves the network's ability to accurately locate and recognize targets [18][19][20][21][22]. However, current channel attention modules simply and crudely use global pooling in the compression Since channel attention in the attention mechanism also has the use of category semantic information, we introduced the channel attention mechanism in the feature fusion phase, as shown in Figure 2, No.…”

Section: Introductionmentioning

confidence: 99%

A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines

Yan,

Li,

Wang

et al. 2023

Entropy

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With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.

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Boundary enhancement and refinement network for camouflaged object detection

Xia,

Cao,

Gao

et al. 2024

Machine Vision and Applications

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Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection

Cited by 6 publications

References 36 publications

Greedy‐based user selection for federated graph neural networks with limited communication resources

Greedy‐based user selection for federated graph neural networks with limited communication resources

A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines

Boundary enhancement and refinement network for camouflaged object detection

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