The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component ...
为了促进特征重用和梯度反向传播,我们为GAM添加了skip concatenation connection。 最后,GCN层用于输出指定数量的特征图,这些特征图可直接用于节点分类预测或用作后续操作的输入。每个GAM仅整合了来自相邻节点的信息,因此堆叠了多个GAM来捕捉图的大部分信息。(参考 Graph Representation Learning via Hard and Channel-Wise ...
traditional CNN’s feature maps are integrated by a concatenation operation. Furthermore, the extensive experiments are conducted on four popular datasets, Shanghai Tech Part A/B, GCC, and UCF_CC_50 Dataset. The results show that the proposed method achieves state-of-the-art results....
In contrast to Group Spatial-Temporal (GST) [47], which uses a hard-wired channel concatenation, we use a self-adaptive and trainable approach to aggregate spatial-temporal features for each block channel. Hence, the new model should be more discerning for different types of video actions. In...
To assess the presence of knowledge in our network, we train a classifier on the concatenation of all network layers (‘ALL’). Our observations show that the classifier trained on the feature vectors generated from the network (‘ALL’) outperforms the majority baseline (‘Maj-C’) ...
We have presented Refined UNet v2, a concatenation of a network backbone and a subsequent embedded conditional random field (CRF) layer, which coarsely performs pixel-wise classification and refines edges of segmentation regions in a one-stage way. However, the CRF layer of v2 employs a gray-...
Finally, two types of attention information and traditional CNN's feature maps are integrated by a concatenation operation. Furthermore, the extensive experiments are conducted on four popular datasets, Shanghai Tech Part A/B, GCC, and UCF_CC_50 Dataset. The results show that the proposed ...
In contrast to Group Spatial-Temporal (GST) [47], which uses a hard-wired channel concatenation, we use a self-adaptive and trainable approach to aggregate spatial-temporal features for each block channel. Hence, the new model should be more discerning for different types of video actions. In...