Ppt: Anomaly Detection Dataset of Printed Products With Templates

This paper presents a visual sentiment prediction method using cross-way few-shot learning based on knowledge distillation. Previous studies on visual sentiment prediction methods have focused only on one sentiment dataset although there are several sentiment datasets following different sentiment theories. Originally, sentiments are abstract notions common to humans regardless of the difference between sentiment theories. Thus, the use of knowledge obtained from several sentiment datasets can be the effective way to realize robust visual sentiment prediction. To collaboratively use sentiment datasets, there are the following two concerns: training of the model introducing different sentiment theories and prediction of a newly given sample whose sentiment theory is unknown. Thus, we focus on knowledge distillation, which can improve the generalization ability of several tasks, and effective training becomes feasible. in addition, to deal with the different numbers of sentiment labels in a test phase, we newly introduce the cross-way few-shot learning scheme into knowledge distillation. The main contribution in this paper is to integrate knowledge distillation and the cross-way approach for the visual sentiment prediction, and this is the first work for dealing with the difference of sentiment datasets used in the training and test phases.