https://arxiv.org/pdf/1904.09146.pdf
- Summary of research on evaluating the salience of images using Deep Learning
The rest of the paper is organized as follows. Section 2 explains the proposed taxonomies, each accompanied with one or two most representative models. Section 3 examines the most notable SOD datasets, whereas Section 4 describes several widely used SOD metrics. Section 5 benchmarks several deep SOD models and provides in-depth analyses. Section 6 provides further discussions and presents open issues and future research directions of the field. Finally, Section 7 concludes the paper.
- Classification based on the architecture of Neural Network
- There are pixel-based methods and CNN-based methods.
- Classification based on the level of supervision
- Additionally, there is a classification of Single Task Learning vs Multi Task Learning (MTL).