  Since 2014 transfer learning has become the key driver for the improvement of
spatial saliency prediction; however, with stagnant progress in the last 3-5
years. We conduct a large-scale transfer learning study which tests different
ImageNet backbones, always using the same read out architecture and learning
protocol adopted from DeepGaze II. By replacing the VGG19 backbone of DeepGaze
II with ResNet50 features we improve the performance on saliency prediction
from 78% to 85%. However, as we continue to test better ImageNet models as
backbones (such as EfficientNetB5) we observe no additional improvement on
saliency prediction. By analyzing the backbones further, we find that
generalization to other datasets differs substantially, with models being
consistently overconfident in their fixation predictions. We show that by
combining multiple backbones in a principled manner a good confidence
calibration on unseen datasets can be achieved. This new model, "DeepGaze IIE",
yields a significant leap in benchmark performance in and out-of-domain with a
15 percent point improvement over DeepGaze II to 93% on MIT1003, marking a new
state of the art on the MIT/Tuebingen Saliency Benchmark in all available
metrics (AUC: 88.3%, sAUC: 79.4%, CC: 82.4%).
