Visual impression estimation system considering attribute information

Detailed identification of visual impressions of objects by attributes can be leveraged to develop products and improve customer satisfaction. In this study, we propose a method to estimate Kansei (affective) information for each attribute, which is the visual impression received from the image. For each attribute, we created a dataset with Kansei indices. By fine-tuning the created dataset to combine attribute information with the output of ResNet18 which was already trained with ImageNet to output indexes, we confirmed that the correlation coefficients for multiple item ratings were higher than those of a deep learning model without attribute information.