Convolution blocks serve as local feature extractors and are the key to success of the neural networks. To make local semantic feature embedding rather explicit, we reformulate convolution blocks as feature selection according to the best matching kernel. In this manner, we show that typical ResNet blocks indeed perform local feature embedding via template matching once batch normalization (BN) followed by a rectified linear unit (ReLU) is interpreted as arg-max optimizer. Following this perspective, we tailor a residual block that explicitly forces semantically meaningful local feature embedding through using label information. Specifically, we assign a feature vector to each local region according to the classes that the corresponding region matches. We evaluate our method on three popular benchmark datasets with several architectures for image classification and consistently show that our approach substantially improves the performance of the baseline architectures.

翻译：卷积块作为局部特征提取器，是神经网络成功的关键。为使局部语义特征嵌入更加显式化，我们将卷积块重新表述为根据最佳匹配核进行的特征选择。通过这种方式，我们表明典型的ResNet块在将批归一化（BN）后接线性整流单元（ReLU）解释为arg-max优化器时，确实通过模板匹配执行局部特征嵌入。基于这一视角，我们定制了一个残差块，通过利用标签信息显式强制进行有语义意义的局部特征嵌入。具体而言，我们根据对应区域匹配的类别为每个局部区域分配一个特征向量。我们在三个流行的基准数据集上使用多种图像分类架构评估了我们的方法，并一致表明我们的方法显著提升了基线架构的性能。