and over 25% of ground truth gaze measurements are missing. It is also interesting to see that the center bias outperforms all saliency-based methods and works only slightly worse than Yin *et al.* [@li2013learning] on GTEA Gaze Plus, which demonstrates the strong spatial bias of gaze in egocentric videos.

  ----------------------------------- ------------- --------------- ------------- ---------------
                                                                                  
  (lr)[2-3]{}(lr)[4-5]{}                AAE (deg)         AUC         AAE (deg)         AUC
  Itti *et al.* [@itti2000saliency]       19.9           0.753          18.4           0.747
  GBVS [@harel2007graph]                  14.7           0.803          15.3           0.769
  SALICON [@huang2015salicon]             15.6           0.818          16.5           0.761
  Center bias                              8.6           0.819          10.2           0.789
  Yin *et al.* [@li2013learning]           7.9           0.867           8.4           0.878
  DFG [@zhang2017deep]                     6.6           0.952          10.5           0.883
  Our full model                       [**4.0**]{}   [**0.957**]{}   [**7.6**]{}   [**0.898**]{}
  ----------------------------------- ------------- --------------- ------------- ---------------

  : Performance comparison of different methods for gaze prediction on two public datasets. Higher AUC (or lower AAE) means higher performance.[]{data-label="table:gazepred"}

### Ablation Study.

To study the effect of each module of our model, and the effectiveness of our modified binary cross entropy loss (Equation \[equa:2df\]), we conduct an ablation study and test each component on both GTEA Gaze Plus and GTEA Gaze datasets. Our baselines include: 1) single-stream saliency prediction with binary cross entropy loss (**S-CNN bce** and **T-CNN bce**), 2) single-stream saliency prediction with our 