In this paper, we proposed a novel multi-feature fusion (MFF) CNNs framework for the Drosophila embryo of interest detection. Considering the great variety of Drosophila embryonic images, the proposed network takes full advantages of multi-level and multi-scale convolutional features by leveraging ...
A four-layer multi-task fusion convolutional neural network (CNN) was developed for feature recognition, the network was well-trained within 30 minutes based on our server. Compared to single-task CNN, multi-task fusion CNN was proved better in classification accuracy for nine of twelve settings...
The second challenge is addressed by a multiview CNN fusion model through a combination layer connecting the representation layers of RGB view and depth view. Comprehensive experiments on four benchmark datasets demonstrate the significant and consistent improvements of the proposed approach over other ...
To this end, a deep convolutional neural network (CNN) trained by high-quality image patches and their blurred versions is adopted to encode the mapping. The main novelty of this idea is that the activity level measurement and fusion rule can be jointly generated through learning a CNN model,...
SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation*... Y Chen,B Zou,Z Guo,... - IEEE 被引量: 0发表: 2023年 基于swin-UNet-denoise 和最小二乘法的两步相位解包裹 Phase unwrapping plays an important...
(CNN). The proposed method contains three parts: short-time gap feature extraction, multi-scale local feature learning, and global feature learning. In the process of short-time gap feature extraction, large kernel filters are employed to extract the features within the short-time gap from the ...
2. Multi-exposure fusion with CNN features [CNN(ICIP 2018)] [Paper] [Code] 3. Deep guided learning for fast multi-exposure image fusion [DeepFuse(MEF-Net(TIP 2020))] [Paper] [Code] 4. Multi-exposure high dynamic range imaging with informative content enhanced network [ICEN(NC 2020)] ...
Employing CNN, SAE, GRU, and LSTM as multi-task learning classification models, training validation and experimental testing were conducted on the QUIC dataset. A comparative analysis with single-task and ensemble learning methods reveals that, in the context of predicting network traffic types, the...
52 proposed an ensemble of CNN models, namely, DenseNet2139, ResNet5018, and InceptionV353 for COVID-19 diagnosis, where individual models’ output their prediction separately and then combined using weighted average for the final prediction. Their model imparts the highest accuracy of 94.00% on...
The mmfCNN can fully use different information provided by diverse modalities, which is based on a weight-adaptation aggregation approach. Specifically, we utilize a multi-layer fusion model to further aggregate the features from different layers, which fuses the low-, mid- and high-level ...