Recent advances in deep neural networks have been developed via architecture
search for stronger representational power. In this work, we focus on the
effect of attention in general deep neural networks. We propose a simple and
effective attention module, named Bottleneck Attention Module (BAM), that can
be integrated with any feed-forward convolutional neural networks. Our module
infers an attention map along two separate pathways, channel and spatial. We
place our module at each bottleneck of models where the downsampling of feature
maps occurs. Our module constructs a hierarchical attention at bottlenecks with
a number of parameters and it is trainable in an end-to-end manner jointly with
any feed-forward models. We validate our BAM through extensive experiments on
CIFAR-100, ImageNet-1K, VOC 2007 and MS COCO benchmarks. Our experiments show
consistent improvement in classification and detection performances with
various models, demonstrating the wide applicability of BAM. The code and
models will be publicly available.

本篇论文提出了一种名为 Bottleneck Attention Module（BAM）的简单有效的注意力机制，可嵌入任何前馈卷积神经网络中，并在 CIFAR-100、ImageNet-1K、VOC 2007 和 MS COCO 等基准测试中通过广泛实验验证了其在图像分类和检测性能上的一致提高。