M2FN: Multi-step Modality Fusion for Advertisement Image Assessment

Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neural networks for this purpose, these studies have not utilized image-related auxiliary attributes, which include embedded text frequently found in ad images. We, therefore, investigated the influence of these attributes on ad image preferences. First, we analyzed large-scale real-world ad log data and, based on our findings, proposed a novel multi-step modality fusion network (M2FN) that determines advertising images likely to appeal to user preferences. Our method utilizes auxiliary attributes through multiple steps in the network, which include conditional batch normalization-based low-level fusion and attention-based high-level fusion. We verified M2FN on the AVA dataset, which is widely used for aesthetic image assessment, and then demonstrated that M2FN can achieve state-of-the-art performance in preference prediction using a real-world ad dataset with rich auxiliary attributes.

Which Ads to Show? Advertisement Image Assessment with Auxiliary Information via Multi-step Modality Fusion

Assessing aesthetic preference is a fundamental task related to human cognition. It can also contribute to various practical applications such as image creation for online advertisements. Despite crucial influences of image quality, auxiliary information of ad images such as tags and target subjects can also determine image preference. Existing studies mainly focus on images and thus are less useful for advertisement scenarios where rich auxiliary data are available. Here we propose a modality fusion-based neural network that evaluates the aesthetic preference of images with auxiliary information. Our method fully utilizes auxiliary data by introducing multi-step modality fusion using both conditional batch normalization-based low-level and attention-based high-level fusion mechanisms, inspired by the findings from statistical analyses on real advertisement data. Our approach achieved state-of-the-art performance on the AVA dataset, a widely used dataset for aesthetic assessment. Besides, the proposed method is evaluated on large-scale real-world advertisement image data with rich auxiliary attributes, providing promising preference prediction results. Through extensive experiments, we investigate how image and auxiliary information together influence click-through rate.