Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study. [PDF]
JMIR Med InformHong T +5 more
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Research on a fault diagnosis method for photovoltaic power plants based on a dual-channel 1D-2D-CNN-BiLSTM spatiotemporal feature fusion network. [PDF]
Sci RepDeng P, Li Z, Huang Z.
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Deep Learning-Based Anatomical Segmentation of the Foot and Ankle: A Multi-View Radiograph Approach. [PDF]
Yonsei Med JLim H +5 more
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Enhancing grape disease detection: A comparative analysis of hybrid CNN-LSTM and CNN methods. [PDF]
MethodsXMulik V, Patil V.
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T-pGNN4DTI: Towards better drug-target interactions prediction using Global Self-attentive Pooled Graph Convolutional Networks and protein pre-training Models. [PDF]
PLoS OneLin Y +6 more
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Sparse fully convolutional network for face labeling
Neurocomputing, 2019 Abstract This paper proposes a sparse fully convolutional network (FCN) for face labeling. FCN has demonstrated strong capabilities in learning representations for semantic segmentation. However, it often suffers from heavy redundancy in parameters and connections.
Shiping Wen +2 more
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Object instance identification with fully convolutional networks
Multimedia Tools and Applications, 2018 This paper presents a novel approach for instance search and object detection, applied to museum visits. This approach relies on fully convolutional networks (FCN) to obtain region proposals and object representation. Our proposal consists in four steps: a classical convolutional network is first fined-tuned as classifier over the dataset, next we ...
Maxime Portaz +4 more
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Fully shared convolutional neural networks
Neural Computing and Applications, 2021Recently, the group convolutions are widely used in mobile convolutional neural networks (CNNs) to improve the model’s efficiency. However, the training process of these popular group-conv mobile models is usually time-consuming compared to the regular models.
Yao Lu 0008 +4 more
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Visual Tracking with Fully Convolutional Networks
2015 IEEE International Conference on Computer Vision (ICCV), 2015We propose a new approach for general object tracking with fully convolutional neural network. Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet.
Lijun Wang +3 more
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