We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
您好,请问“将z中的m个局部相似度值进行线性加权,得到查询图像与类别之间的全局相似度Z”,请问这个权重是如何计算的呢?
The text was updated successfully, but these errors were encountered:
您好,这个问题问的非常好,我们其实是采用了一个比较巧妙的方式来实现这个可学习权重w,
我们在计算查询图像Q与某个类别的相似度的时候,假设Q有m个局部相似度,我们直接把这m个相似度存下来,放在mea_sim里,它的维度是"类别数量*m",然后在self.classifier里我们采用了一个Conv1d来,使得Kernel size和stride都等于m,即论文中的441,通过这种方式就自动学习了w
nn.Conv1d(1, 1, kernel_size=441, stride=441, bias=use_bias)
Sorry, something went wrong.
您好,这个问题问的非常好,我们其实是采用了一个比较巧妙的方式来实现这个可学习权重w, 我们在计算查询图像Q与某个类别的相似度的时候,假设Q有m个局部相似度,我们直接把这m个相似度存下来,放在mea_sim里,它的维度是"类别数量*m",然后在self.classifier里我们采用了一个Conv1d来,使得Kernel size和stride都等于m,即论文中的441,通过这种方式就自动学习了w nn.Conv1d(1, 1, kernel_size=441, stride=441, bias=use_bias)
好的明白了,谢谢您!
No branches or pull requests
您好,请问“将z中的m个局部相似度值进行线性加权,得到查询图像与类别之间的全局相似度Z”,请问这个权重是如何计算的呢?
The text was updated successfully, but these errors were encountered: