the current state of the art (SALICON [@huang2015salicon]).

-   *Center bias*: Since egocentric gaze data is observed to have a strong center bias, we use the image center as the predicted gaze position as in [@li2013learning].

-   *Gaze prediction algorithms*: We also compare our method with two state-of-the-art gaze prediction methods: the egocentric cue-based method (Yin *et al.* [@li2013learning]), and the GAN-based method (DFG [@zhang2017deep]). Note that although the goal of [@zhang2017deep] is gaze anticipation in future frames, it also reported gaze prediction in the current frame.

### Performance Comparison.

The quantitative results of different methods on two datasets are given in Table \[table:gazepred\]. Our method significantly outperforms all baselines on both datasets, particularly on the AAE score. Although there is only a small improvement on the AUC score, it can be seen that previous method of DFG [@zhang2017deep] has already achieved quite high score and the space of improvement is limited. Besides, we have observed from experiments that high AUC score does not necessarily mean high performance of gaze prediction. The overall performance on GTEA Gaze is lower than that on GTEA Gaze Plus. The reason might be that the number of training samples in GTEA Gaze is smaller 