To address these issues, we propose a single image deblurring method, in which two modules to fuse multiple features learned in encoder (the Cross-layer Feature Fusion (CFF) module) and manipulate the features after decoder (the Consecutive Attention Module (CAM)) are specially designed, ...
To solve these problems, an AMI intrusion detection model based on the cross-layer feature fusion of a convolutional neural networks (CNN) and long short-term memory (LSTM) networks is proposed in the present work. The model is composed of CNN and LSTM components connected in the form of a...
Firstly, we design a cross-layer feature fusion (CLFF) module to generate discriminative features by fusing and refining the multi-level side-output features with similar characteristics in the same feature group. Secondly, we design a uniqueness enhancement (UE) strategy to respectively emphasize ...
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This module first introduces the Cross-Layer Feature Fusion Block to achieve the feature fusion between the shallow layer and the deep layer, which solves the problem of important information loss during the encoding operation. And it then introduces the Channel Attention Block into the decoder ...
This is mainly due to the feature maps extracted from the CNN and the quality of the region proposals. We present a cross-layer fusion feature network (CLFF-Net) for both high-quality region proposal generation and accurate object detection. The CLFF-Net is based on the cross-layer fusion...
Moreover, the most excellent innovation of our work belongs to the effective cross-layer feature fusion method, which maintains robust feature fusion and information interaction capabilities; in addition, it simplifies redundant parameters compared with the baseline model. To ...
Cross-layer deep-feature fusion module (CFFM)Color space transformationSoftmax lossCenter lossAccurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn ...
Moreover, we use cross-layer feature fusion to enhance the attention on shallow feature maps. By visualizing the features of different layers, we demonstrate the importance of the fusion operation in our method. Our experimental results on the CIFAR-100, tinyImageNet and miniImageNet datasets ...
Honeycomb lung segmentationMulti-scale inputCross-layer feature fusionBi-directional attention gateNETAccurate segmentation of honeycomb lung lesions from lung CT images plays a crucial role in the diagnosis and treatment of various lung diseases. However, the availability of a...