Results 31 to 40 of about 5,438,478 (196)
Hierarchical layout-aware graph convolutional network for unified aesthetics assessment [PDF]
, 2021 Learning computational models of image aesthetics can have a substantial impact on visual art and graphic design. Although automatic image aesthetics assessment is a challenging topic by its subjective nature, psychological studies have confirmed a ...
Lai, Yu-kun +7 more
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Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
Advances in neural information processing systems, 2020 Graph convolutional networks (GCNs) have shown promising results in processing graph data by extracting structure-aware features. This gave rise to extensive work in geometric deep learning, focusing on designing network architectures that ensure neuron activations conform to regularity patterns within the input graph.
Yimeng Min, Frederik Wenkel, Guy Wolf
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Spatial‐temporal slowfast graph convolutional network for skeleton‐based action recognition
IET Computer Vision, 2022 In skeleton‐based action recognition, the graph convolutional network (GCN) has achieved great success. Modelling skeleton data in a suitable spatial‐temporal way and designing the adjacency matrix are crucial aspects for GCN‐based methods to capture ...
Zheng Fang +4 more
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The combination model of CNN and GCN for machine fault diagnosis.
PLoS ONE, 2023 Learning powerful discriminative features is the key for machine fault diagnosis. Most existing methods based on convolutional neural network (CNN) have achieved promising results.
Qianqian Zhang +3 more
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Permutohedral-GCN: Graph Convolutional Networks with Global Attention
CoRR, 2020 Graph convolutional networks (GCNs) update a node's feature vector by aggregating features from its neighbors in the graph. This ignores potentially useful contributions from distant nodes. Identifying such useful distant contributions is challenging due to scalability issues (too many nodes can potentially contribute) and oversmoothing (aggregating ...
Hesham Mostafa, Marcel Nassar
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Frontiers in Genetics, 2023 Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. However, it is challenging for a single model to learn an effective representation within and across spatial ...
Xinxing Li +4 more
doaj +1 more source
HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation
IEEE Transactions on Knowledge and Data Engineering, 2022 An efficient solution to the large-scale recommender system is to represent users and items as binary hash codes in the Hamming space. Towards this end, existing methods tend to code users by modeling their Hamming similarities with the items they historically interact with, which are termed as the first-order similarities in this work.
Han Liu +3 more
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Fusion-GCN: Multimodal Action Recognition Using Graph Convolutional Networks [PDF]
, 2021 In this paper, we present Fusion-GCN, an approach for multimodal action recognition using Graph Convolutional Networks (GCNs). Action recognition methods based around GCNs recently yielded state-of-the-art performance for skeleton-based action recognition.
Michael Duhme +2 more
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DNA-GCN: Graph Convolutional Networks for Predicting DNA-Protein Binding [PDF]
, 2021 Predicting DNA-protein binding is an important and classic problem in bioinformatics. Convolutional neural networks have outperformed conventional methods in modeling the sequence specificity of DNA-protein binding. However, none of the studies has utilized graph convolutional networks for motif inference.
Yuhang Guo 0002 +3 more
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Proceedings of the 2020 12th International Conference on Machine Learning and Computing, 2020 Knowledge graphs display various entities and their relationships in the real world based on knowledge representation and can analyze and predict the intrinsic relationship between knowledge embeddings through data mining and information processing, which are widely used in search engines, web analytics and smart recommendation areas.
Anqi Tian +4 more
openaire +2 more sources