For example, the squeeze-and-excitation (SE) module [@senet] uses global average pooling and a linear layer to compute a scaling factor for each channel and then scales the channels accordingly. SE-enhanced models achieved state-of-the-art performance on image classification and substantial improvements on scene segmentation and object detection. On top of SE, CBAM [@cbam] added global max pooling beside global average pooling and an extra spatial attention submodule. It further improved SE’s performance.

Despite both names containing attention, dot-product attention and scaling attention are two completely separate sets of techniques with very different goals. When appropriate, one might take both techniques and let them work in conjunction. Therefore, it is unnecessary to make any comparison of efficient attention with scaling attention techniques.

Object Detection
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Since the introduction of R-CNN [@rcnn], deep learning methods have achieved remarkable progress on object detection. Mask R-CNN [@mask-rcnn] is one of the most successful deep detector recently. It is efficient and accurate and supports instance segmentation in addition to object detection. Because of these advantages and that [@nonlocal] used Mask R-CNN as their baseline, this work also selected Mask R-CNN as the baseline.

Feature pyramid [@fpn] is a module specifically for object detection that aggregates features 