  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.
