AP.

  Layer(s)                 EA module     Non-local         Input size
  ---------- ----------------------- ------------- ------------------
  None                   $39.4/35.1$   $39.4/35.1$                N/A
  res3                   $40.2/36.0$   $40.3/35.9$     $56 \times 80$
  fpn1                   $39.9/35.8$           OOM   $224 \times 320$
  fpn2                   $39.7/35.7$           OOM   $112 \times 160$
  fpn3                   $39.7/35.5$   $39.8/35.5$     $56 \times 80$
  Best         $\bm{41.2}/\bm{36.7}$   $40.3/35.9$                N/A

  : **Comparison between the efficient attention and non-local modules.** Each numeric cell is in the format box AP/mask AP. The best configuration for the efficient attention module inserted at FPN layers 1-5 and backbone layers res3,4. The best for the non-local module inserted at res3.[]{data-label="tab:vs-ca"}

Table \[tab:vs-sota\] compares efficient attention-augmented detectors with other state-of-the-art methods on the MS-COCO 2017 dataset. EA Mask R-CNN with a ResNeXt-101 backbone set a new state-of-the-art. The version with a ResNet-101 backbone also outperformed all other ResNet-101-based models.

  Model                        Backbone                       AP
  ---------------------------- ------------------- -------------
  TDM Faster R-CNN [@tdm]      Inc.-ResNet-v2             $36.8$
  Mask R-CNN [@mask-rcnn]      ResNeXt-101                $39.8$
  Soft-NMS [@softnms]          Algn.-Inc.-ResNet          $40.9$
  LH R-CNN [@lh-rcnn]          ResNet-101                 $41.5$
  Fitness NMS [@fitness-nms]   ResNet-101                 $41.8$
  **EA Mask R-CNN**            ResNet-101                 $43.1$
  **EA Mask R-CNN**            ResNeXt-101           $\bm{44.9}$

  : **Comparison with the state-of-the-art on MS-COCO 2017.** *Inc.-ResNet-v2* and *Algn.-Inc.-ResNet* represent Inception-ResNet-v2 and Aligned-Inception-ResNet, respectively.[]{data-label="tab:vs-sota"}

### Stereo Depth Estimation {#sec:vs-stereo}

This section will present the experimental results of efficient attention-augmented models for 