stereo depth estimation. The experiments used the Scene Flow dataset. The baseline is the state-of-the-art published model, PSMNet [@psmnet]. The experiments empirically determined an optimal set of hyperparamters, which includes a batch size of 24, a learning rate of 2e-3, and 100 training epochs. Other hyperparamters remain the same as in [@psmnet].

  Model                          EPE
  ------------------- --------------
  PSMNet (original)           $1.09$
  PSMNet (baseline)          $0.513$
  **EA-PSMNet**         $\bm{0.477}$
  Nonlocal-PSMNet                OOM

  : **Experiments on the Scene Flow dataset.** EA-PSMNet is the proposed approach. *OOM* indicates out-of-memory errors.[]{data-label="tab:vs-ca-stereo"}

As Table \[tab:vs-ca-stereo\] shows, the proposed EA-PSMNet demonstrated significant improvement over the highly competitive baseline, which already surpassed the former state-of-the-art by a substantial margin. Table \[tab:vs-sota-stereo\] compares EA-PSMNet with other state-of-the-art methods. EA-PSMNet set a new state-of-the-art on the Scene Flow dataset.

  Model                                EPE
  -------------------------- -------------
  iResNet-i2 [@crl]                 $1.40$
  PSMNet [@psmnet]                  $1.09$
  EdgeStereo [@edgestereo]          $1.12$
  CSPN [@cspn]                      $0.78$
  **EA-PSMNet**                $\bm{0.48}$

  : **Comparison with the state-of-the-art on the Scene Flow dataset.** *EPE* stands for end-point error and is lower the better. The table rounded EPE for EA-PSMNet to the second decimal place following the format of previous works.[]{data-label="tab:vs-sota-stereo"}

### Image Classification {#sec:vs-class}

This section presents the experimental results of efficient attention-augmented models on the ImageNet dataset for image 