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Title: GitHub - woosual/multiModalityFusionForClassification: 多模态数据融合：为了完成多模态数据融合，首先利用VGG16网络和cifar10数据集完成多输入网络的分类，在VGG16的基础之上，将前三层特征提取网络作为不同输入的特征提取网络，在中间层进行特征拼接，后面的卷积层用于提取融合特征，最后加上全连接层。该网络稍作修改就能同时提取两张对应的图片作为输入，在特征提取之后进行融合用于分类。

Open Graph Title: GitHub - woosual/multiModalityFusionForClassification: 多模态数据融合：为了完成多模态数据融合，首先利用VGG16网络和cifar10数据集完成多输入网络的分类，在VGG16的基础之上，将前三层特征提取网络作为不同输入的特征提取网络，在中间层进行特征拼接，后面的卷积层用于提取融合特征，最后加上全连接层。该网络稍作修改就能同时提取两张对应的图片作为输入，在特征提取之后进行融合用于分类。

X Title: GitHub - woosual/multiModalityFusionForClassification: 多模态数据融合：为了完成多模态数据融合，首先利用VGG16网络和cifar10数据集完成多输入网络的分类，在VGG16的基础之上，将前三层特征提取网络作为不同输入的特征提取网络，在中间层进行特征拼接，后面的卷积层用于提取融合特征，最后加上全连接层。该网络稍作修改就能同时提取两张对应的图片作为输入，在特征提取之后进行融合用于分类。

Description: 多模态数据融合：为了完成多模态数据融合，首先利用VGG16网络和cifar10数据集完成多输入网络的分类，在VGG16的基础之上，将前三层特征提取网络作为不同输入的特征提取网络，在中间层进行特征拼接，后面的卷积层用于提取融合特征，最后加上全连接层。该网络稍作修改就能同时提取两张对应的图片作为输入，在特征提取之后进行融合用于分类。 - woosual/multiModalityFusionForClassification

Open Graph Description: 多模态数据融合：为了完成多模态数据融合，首先利用VGG16网络和cifar10数据集完成多输入网络的分类，在VGG16的基础之上，将前三层特征提取网络作为不同输入的特征提取网络，在中间层进行特征拼接，后面的卷积层用于提取融合特征，最后加上全连接层。该网络稍作修改就能同时提取两张对应的图片作为输入，在特征提取之后进行融合用于分类。 - woosual/multiModalityFusionFo...

X Description: 多模态数据融合：为了完成多模态数据融合，首先利用VGG16网络和cifar10数据集完成多输入网络的分类，在VGG16的基础之上，将前三层特征提取网络作为不同输入的特征提取网络，在中间层进行特征拼接，后面的卷积层用于提取融合特征，最后加上全连接层。该网络稍作修改就能同时提取两张对应的图片作为输入，在特征提取之后进行融合用于分类。 - woosual/multiModalityFusionFo...

Opengraph URL: https://github.com/woosual/multiModalityFusionForClassification

X: @github

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