modified bce loss (**S-CNN** and **T-CNN**), 3) two-stream saliency prediction with bce loss (**SP bce**), 4) two-stream input saliency prediction with our modified bce loss (**SP**), 5) the attention transition module (**AT**), and our full model.

Table \[table:ablation\] shows the results of the ablation study. The comparison of the same framework with different loss functions shows that our modified bce loss function is more suitable for the training of gaze prediction in egocentric video. The SP module performs better than either of the single-stream saliency prediction (S-CNN and T-CNN), indicating that both spatial and temporal information are needed for accurate gaze prediction. It is important to see that the AT module performs competitively or better than the SP module. This validates our claim that learning task-dependent attention transition is important in egocentric gaze prediction. More importantly, our full model outperforms all separate components by a large margin, which confirms that the bottom-up visual saliency and high-level task-dependent attention are complementary cues to each other and should be considered together in modeling human attention.

  ------------------------ -------------- --------------- -------------- ---------------
                                                                         
  (lr)[2-3]{}(lr)[4-5]{}     AAE (deg)          AUC         AAE (deg)          AUC
  S-CNN (bce)                   5.61           0.893           9.90           0.854
  T-CNN (bce)                   6.15           0.906          10.08           0.854
  S-CNN                         5.57           0.905           