  We propose an end-to-end-trainable feature augmentation module built for
image classification that extracts and exploits multi-view local features to
boost model performance. Different from using global average pooling (GAP) to
extract vectorized features from only the global view, we propose to sample and
ensemble diverse multi-view local features to improve model robustness. To
sample class-representative local features, we incorporate a simple auxiliary
classifier head (comprising only one 1$\times$1 convolutional layer) which
efficiently and adaptively attends to class-discriminative local regions of
feature maps via our proposed AdaCAM (Adaptive Class Activation Mapping).
Extensive experiments demonstrate consistent and noticeable performance gains
achieved by our multi-view feature augmentation module.
