Incremental Learning of Multi-Tasking Networks for Aesthetic Radar Map Prediction

• Main Authors: Xin Jin, Xinghui Zhou, Xiaodong Li, Xiaokun Zhang, Hongbo Sun, Xiqiao Li, Ruijun Liu
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Neural network; multitasking; computer vision; incremental learning
• Online Access: https://ieeexplore.ieee.org/document/8936442/

It is difficult and challenging to evaluate the aesthetics quality of images from multiple angles. Since humans' perception of images comes from many factors, the integrated image aesthetic quality assessment cannot be easily summarized by few attributes. A comprehensive evaluation is supposed to predict many aesthetic attributes across not only one dataset. This requires the model to have not only high accuracy, but also strong generalization ability, resulting in a better prediction on multiple models and datasets. Recent work shows that deep convolution neural network can be used to extract image features and further evaluate the total score of images, and the method of evaluation are lacking of sufficient detailed features. In this paper, we propose a multi-task convolution neural network with more incremental features. We show the results in the way of a hexagon map, which is called aesthetic radar map. This allows the network model to fit different attributes in various datasets better.