BAM: Bottleneck Attention Module
This work addresses the need for effective attention mechanisms in general feed-forward convolutional neural networks, offering a domain-specific improvement for computer vision tasks.
The paper tackles the problem of enhancing deep neural networks by introducing the Bottleneck Attention Module (BAM), a simple attention module that improves classification and detection performance across multiple benchmarks like CIFAR-100 and ImageNet-1K.
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.