Visual attention selects data considered as “interesting” by humans, and it is modeled in the field of engineering by feature-engineered methods finding contrasted/surprising/unusual image data. Deep learning drastically improved the models efficiency on the main benchmark datasets. However, Deep Neural Networks-based (DNN-based) models are counterintuitive: surprising or unusual data are by definition difficult to learn because of their low occurrence probability. In reality, DNN-based models mainly learn top-down features such as faces, text, people, or animals which usually attract human attention, but they have low efficiency in extracting surprising or unusual data in the images. In this article, we propose a new family of visual attention models called DeepRare and especially DeepRare2021 (DR21), which uses the power of DNNs’ feature extraction and the genericity of feature-engineered algorithms. This algorithm is an evolution of a previous version called DeepRare2019 (DR19) based on this common framework. DR21 (1) does not need any additional training other than the default ImageNet training, (2) is fast even on CPU, (3) is tested on four very different eye-tracking datasets showing that DR21 is generic and is always within the top models on all datasets and metrics while no other model exhibits such a regularity and genericity. Finally, DR21 (4) is tested with several network architectures such as VGG16 (V16), VGG19 (V19), and MobileNetV2 (MN2), and (5) it provides explanation and transparency on which parts of the image are the most surprising at different levels despite the use of a DNN-based feature extractor.

Human visual system is modeled in engineering field providing feature-engineered methods which detect contrasted/surprising/unusual data into images. This data is "interesting" for humans and leads to numerous applications. Deep learning (DNNs) drastically improved the algorithms efficiency on the main benchmark datasets. However, DNN-based models are counter-intuitive: surprising or unusual data is by definition difficult to learn because of its low occurrence probability. In reality, DNNs models mainly learn top-down features such as faces, text, people, or animals which usually attract human attention, but they have low efficiency in extracting surprising or unusual data in the images. In this paper, we propose a model called DeepRare2019 (DR) which uses the power of DNNs feature extraction and the genericity of feature-engineered algorithms. DR 1) does not need any training, 2) it takes less than a second per image on CPU only and 3) our tests on three very different eye-tracking datasets show that DR is generic and is always in the top-3 models on all datasets and metrics while no other model exhibits such a regularity and genericity. DeepRare2019 code can be found at https://github.com/numediart/VisualAttention-RareFamily