为了捕捉多假设之间的相关性,进行交叉假设交互,作者提出了MH-CA,MH-CA由多个平行的multi-head cross attention(MCA)组成。 MCA用于衡量多个假设特征之间的相关性,与MSA有着相似的结构。下图右侧为MCA的结构图。在MCA中,如果使用相同的keys和values作为输入,会导致更多的块,此处作者采用了一种更有效的策略,通过使用不...
为了解决这些挑战，我们引入了Deformable Large Kernel Attention (D-LKA Attention)}的概念，这是一种采...
为了捕获多个假设彼此之间的相关性以进行交叉假设通信,提出多个多头交叉注意(MCA)元素并行组成的MH-CA。 如图是一点儿NN架构细节:左边是Multi-head self-attention(MHA),右边是Multihead cross-attention (MCA),二者结果相似。 MHSA旨在独立地捕获每个假设的单假设相关性,以便进行自假设通信。这里,MCA采用了一种更有效...
Experimental results on three recent methods demonstrate that the proposed Multi-head Cross-modal Attention (MCA) mechanism can significantly improve the performance of these methods, and even achieve state-of-the-art results on the THUMOS14 and ActivityNet1.2 datasets....
如图是一点儿NN架构细节:左边是Multi-head self-attention(MHA),右边是Multihead cross-attention (MCA),二者结果相似。MHSA旨在独立地捕获每个假设的单假设相关性,以便进行自假设通信。这里,MCA采用了一种更有效的策略,通过不同的输入(M个MCA块)来减少参数的数量。
Source code of paper: "MCANet: Shared-weight-based MultiheadCrossAttention network for drug-target interaction prediction" - MrZQAQ/MCANet
Left: Multi-head self-attention (MSA). Right: Multi- head cross-attention (MCA). of different hypotheses are fed into several parallel MSA blocks, which can be expressed as: Zlm = Zlm−1 + MSAm(LN(Zlm−1)), (4) where l∈[1, ..., L2] is the inde...
MCA-Net: Multi-scale comprehensive attention applicaton of CNN in medical image segmentation[J]. Microelectronics & Computer, 2022, 39(3): 71-77. DOI: 10.19304/J.ISSN1000-7180.2021.0950 Citation: DING Caifu, YANG Chen, JI Qiulang, WANG Yang, ZHANG Bing. MCA-Net: Multi-scale ...
🐛 Describe the bug I export my custom module (which is a simple wrapper around torch.nn.MultiheadAttention) into .onnx using the following code: import numpy as np import onnx import onnxruntime as ort import torch class MHAWrapper(torch...
MCANet: Hierarchical cross-fusion lightweight transformer based on multi-ConvHead attention for object detection ? 2023 The AuthorsThe visual Transformer model based on self-attention has achieved better performance than convolutional neural networks in object detecti... Z Zhao,K Hao,Liu X.Zheng T....