result reinforces the hypothesis in Section \[sec:intro\] that most attention maps are expressible as linear combinations of a limited set of basis attention maps. Therefore, researchers can reduce the dimensionality of the keys and queries in efficient attention modules, if there is a need to further save resources.

    $d_k$        Box AP       Mask AP
  ------- ------------- -------------
     $32$        $40.4$        $36.1$
     $64$   $\bm{40.6}$   $\bm{36.2}$
    $128$        $40.3$        $36.1$

  : **Experiments with different dimensionalities of the keys.** All experiments inserted efficient attention modules at FPN levels 1-5.[]{data-label="tab:channels"}

### Backbone Architecture

This section reports experiments with different backbone networks. Table \[tab:backbone\] reports the results using three selected architectures, ResNet-50, ResNet-101 and ResNeXt-101. The significant and robust performance gains across all three backbones demonstrate the generalizability of efficient attention.

  Backbone        Baseline APs   With EA modules
  ------------- -------------- -----------------
  ResNet-50        $39.4/35.1$       $41.2/36.7$
  ResNet-101       $41.3/36.6$       $43.1/37.9$
  ResNeXt-101      $43.5/38.5$       $44.9/39.5$

  : **Experiments with different backbone architectures.** All results have format box AP/mask AP. The *with EA modules* variants inserted the modules at fpn1-5 & res3,4. Experiments for ResNet-101 and ResNeXt-101 used batch sizes of 32, not 64.[]{data-label="tab:backbone"}

Visualization {#sec:vis}
-------------

![**Visualization of global attention maps.** The left-most column displays 4 images from MS-COCO 2017. The other three columns show 