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DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling
[article]
2021
arXiv
pre-print
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
arXiv:2105.12441v3
fatcat:srjm65isxnbt3imgri2vvcfdjm