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WenbinLee avatar WenbinLee commented on June 24, 2024

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)

from covamnet.

WriteCodee avatar WriteCodee commented on June 24, 2024

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！

from covamnet.

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