DeepRare: Generic Unsupervised Visual Attention Models

Abstract

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.

Nighttime Driver Behavior Prediction Using Taillight Signal Recognition via CNN-SVM Classifier

“…In this issue, the ImageNet dataset is selected. Similar to [37], the fully connected and pooling layer from the VGG-19 network is removed. Then, in each layer and for each feature, the data rarity is calculated [38].…”