在Yolov5中,我们可以看到引入了多种注意力机制,如CBAM (Convolutional Block Attention Module)、SE (Squeeze-and-Excitation)、ECA (Efficient Channel Attention)、CA (Channel Attention)、SimAM (Similarity-based Attention Mechanism)、ShuffleAttention、Criss-CrossAttention以及CrissCrossAttention等。这些注意力机制各有...
CCNet: Criss-Cross Attention for Semantic Segmentation读书笔记 Criss-Cross Network(CCNet): 作用: 用来获得上下文信息,具体来说,对于每一个像素,CCNet中的criss-cross注意模块把水平和竖直方向上的语义信息聚合起来,对局部特征图中的每一个像素的交叉路径上获取所有像素的上下文信息。使用两个连续的交叉注意模块......
Bi-attention mechanismEnsemble empirical mode decompositionWind power predictionHybrid modelCrisscross optimization algorithmAccurate wind power forecasting is of great significance for power system operation. In this study, a triple-stage multi-step wind power forecasting approach is proposed by applying ...
1、GPU memory friendly. Compared with the non-local block, the recurrent criss-cross attention module requires 11× less GPU memory usage.阡陌注意力模块与使用non-local模块比,GPU内存减少11倍。 2、High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 8...
CCNet: Criss-Cross Attention for Semantic Segmentation Zilong Huang1∗, Xinggang Wang1†, Lichao Huang2, Chang Huang2, Yunchao Wei3,4, Wenyu Liu1 1School of EIC, Huazhong University of Science and Technology 2Horizon Robotics 3ReLER, UTS 4Beckman Institute, University of Illin...
1、GPU memory friendly. Compared with the non-local block, the recurrent criss-cross attention module requires 11× less GPU memory usage.阡陌注意力模块与使用non-local模块比,GPU内存减少11倍。 2、High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about ...
The details of criss-cross large kernel (CCLK) module. The CCLK module contains two 1D convolutions with self-attention mechanism in orthogonal directions, and enhances the representation by aggregating local detail features and self-attention mechanism. The symbols Ⓣ, ⊙, and ⓧ denote transpos...
Additionally, a Dynamic Criss-Cross Attention (DCCA) mechanism is proposed in the decoder of the U-Net-based generator to extract both local and global 鈥 eatures of plane-wave images while avoiding interference caused by irrelevant regions. RESULTS: In the reconstruction of point targets, the ...
We apply ResNet-50 to extract the features of the templateimage and search region, then feed the feature maps into a recurrent criss-cross attentionmodule to make it more discriminative. The enhanced feature maps are inputted into ourimproved head network, which include the center-ness branch ...
Then, within the self-attentive mechanism, an efficient interactive spatial module was designed in our multi-headed attention mechanism to obtain a more comprehensive association of the target global contextual information of crop diseases. Next, an efficient Criss-cross window transformer module is ...