Graph neural networks (GNN) has been demonstrated to be effective in classifying graph structures. To further improve the graph representation learning ability, hierarchical GNN has been explored. It leverages the differentiable pooling to cluster nodes into fixed groups, and generates a coarse-grained...
因此本文使用无监督的Skip-gram模型预训练词嵌入,然后从中挑选出候选单词和其上下文的tokens的词嵌入,组合在一起形成词汇特征向量。 2.2 句子特征表示 使用DMCNN动态多池层根据事件触发词和论元对句子进行划分,保留每个部分的关键信息,以便提取出论元和触发词之间的语义联系。 输入:由三种输入拼接而成,分别是词特征(CWF...
Hu Y, Gao J, Xu C (2020) Learning Dual-Pooling Graph Neural Networks for Few-shot Video Classification, IEEE Trans Multimedia (Early Access). https://doi.org/10.1109/TMM.2020.3039329 Ilg E, Mayer N, Saikia T et al (2017) FlowNet 2.0: evolution of optical flow estimation with deep netw...
Notes: HGNN consists oftwo-level graph neural networks; in the low level, anintra-task GNNis responsible of learning a powerful representation for each data point in a task by aggregating its neighbors. Based on the learned representation, atask embeddingcan be generated for each task in a si...
Fig. 3. Diagram of multi-kernel-size channel attention method. In order to take full advantage of the relationship between the feature planes generated by each convolution kernel, we first squeeze the global spatial information into a channel descriptor. That is, the global averaging pooling is ...
In the input-level fusion strategy, multi-modality images are fused channel by channel as the multi-channel inputs to learn a fused feature representation, and then to train the segmentation network. Most of the existing multi-modal medical image segmentation networks adopt the input-level fusion...
Next, we modify the centered-instance network to additionally predict class probabilities for the crop in the same fashion as previously described neural network-based identity-tracking approaches8. We project features from the deepest layer of the encoder into a 2D global maximum pooling layer, ...
(Fig.1)for mult-station earthquake monitoring. To utilize the GNN, we first need to change the data from the matrix format to the graph format and employ a graph-based representation of the stations, where each station is represented as a node in the graph and the three-channel data and...
For instance, the encoder features and decoder features connected by the first skip-connection have a distinct semantic gap, since the first feature level of the encoder is only computed by fewer convolutional and pooling layers, while the corresponding feature level in the decoder has higher ...
The model is a combination of consecutive multiscale feature learning (CMSFL) modules for extracting features from an image, a max-pooling operation for decreasing the spatial dimension of an image, and a fully connected dense layer for linearly classifying an image into one of the pre-defined ...