A novel autoencoder\nbased multiscale attention mechanism is incorporated with EANet that\nfeeds on both the OCT image and edge-enhanced OCT image at every level of\nthe encoder. The proposed network, EANet, has been trained and tested for\nthe segmentation of all three types of fluids on...
In this paper, a multiscale residual deep neural network CSA-MResNet model based on the channel spatial attention mechanism is proposed. Firstly, the residual network is integrated into a multiscale manner to obtain the characteristics of ECG data at different scales and then increase the channel...
Meanwhile, the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel–spatial attention mechanism to extract multiscale features, concentrating on deep fault features. During training, a weighting mechanism is introduced to prioritize domain samples with clear fault...
First, this paper proposes amultiscale feature cascaded attention (MCFA) module, which extracts multiscale feature information throughmultiple continuous convolution paths, and uses doubleattention to realize multiscale feature information fusion of different paths. Second, the attention-gate mechanism is ...
[20] proposed a method that combines the attention mechanism with multiple scales, combines local features with their corresponding global features, and adaptively emphasizes the interdependence between them to segment images. In 2020, Yan et al. [21] proposed a multiscale neural architecture search ...
In addition, an efficient pyramidal multi-scale channel attention mechanism is designed to highlight the multi-scale spatial information and the boundary features. Finally, a residual structure is integrated to sum the elements from the input feature map to alleviate degradation during training. Sign ...
Through experiments on the COCO dataset, it can be seen that the use of a multiscale FPN mechanism not only solves the problem of center point overlap but also helps improve the accuracy of detection. CenterNet Plus can be used to greatly improve the detection accuracy on the premise of ...
multi-view deep learning model with channel-aware attention mechanism, to express multi-channel EEG signals in a high-level space with interpretable meanings... Y Ye,G Xun,F Ma,... - IEEE 被引量: 6发表: 2018年 A trans-disciplinary review of deep learning research for water resources scien...
Finally, we employ a fusion-attention mechanism to integrate different features and predict the optimal label for each word in the input sequence. This section details our proposed NER model, SEMFF-NER. The model comprises three modules: the global semantic encoder, multi-scale local feature ...
Meanwhile, the pyramid attention mechanism (PAM) in PRCM, which adjusts the weight of hybrid pyramid attention vector to refine spatial features of low‐level, is developed to alleviate the semantic gap. Moreover, the LIFM is designed to detect objects at multiple scales from the global‐...