Feature fusion, the combination of features from different layers or
branches, is an omnipresent part of modern network architectures. It is often
implemented via simple operations, such as summation or concatenation, but this
might not be the best choice. In this work, we propose a uniform and general
scheme, namely attentional feature fusion, which is applicable for most common
scenarios, including feature fusion induced by short and long skip connections
as well as within Inception layers. To better fuse features of inconsistent
semantics and scales, we propose a multi-scale channel attention module, which
addresses issues that arise when fusing features given at different scales. We
also demonstrate that the initial integration of feature maps can become a
bottleneck and that this issue can be alleviated by adding another level of
attention, which we refer to as iterative attentional feature fusion. With
fewer layers or parameters, our models outperform state-of-the-art networks on
both CIFAR-100 and ImageNet datasets, which suggests that more sophisticated
attention mechanisms for feature fusion hold great potential to consistently
yield better results compared to their direct counterparts. Our codes and
trained models are available online.

本文提出了一种统一且普遍的方案，即关注特征融合，它适用于大多数常见场景，包括通过短跳线和长跳线引起的特征融合以及在 Inception 层内部的特征融合。通过多尺度通道关注模块来更好地融合不一致的语义和尺度的特征，并通过迭代关注特征融合来缓解特征地图的初始集成成为瓶颈的问题，我们的模型在 CIFAR-100 和 ImageNet 数据集上均优于最先进的网络，并表明特征融合的更复杂的关注机制具有持续提供比直接特征融合更好地结果的巨大潜力。