AdSEE：探究图像风格编辑对广告吸引力的影响
AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness
AI总结：
本文提出AdSEE框架，通过引入StyleGAN语义编辑和遗传广告编辑器优化广告封面图像风格，并利用QQ-AD数据集训练点击率预测器，验证了图像风格编辑能显著提升在线广告的点击率。
摘要：
在线广告是电子商务网站、社交媒体平台和搜索引擎中的重要元素。随着移动浏览的日益普及，除了文本描述外，许多在线广告还以封面图像的形式展示视觉信息，以吸引用户的注意力。最近的各种研究集中在预测感知视觉特征的在线广告点击率，或组合最佳广告元素以增强可见性。在本文中，我们提出了广告风格编辑与吸引力增强（AdSEE），探讨对广告图像进行语义编辑是否能影响或改变在线广告的受欢迎程度。我们将基于StyleGAN的面部语义编辑和反演技术引入广告图像，并训练了一个点击率预测器，该预测器除了将传统的视觉和文本特征归因于点击率外，还将基于GAN的人脸潜在表示归因于点击率。通过一个名为QQ-AD的大型收集数据集（包含20,527个在线广告），我们进行了广泛的离线测试，以研究不同的语义方向及其编辑系数如何影响点击率。我们进一步设计了一个遗传广告编辑器，用于在给定输入广告封面图像的情况下，高效搜索最佳的编辑方向和强度，以提升其预测点击率。在5天内进行的在线A/B测试验证了与原始广告对照组相比，AdSEE编辑样本的点击率有所提高，从而验证了图像风格与广告受欢迎程度之间的关系。我们在https://github.com/LiyaoJiang1998/adsee开源了AdSEE研究的代码。
英文摘要：
Online advertisements are important elements in e-commerce sites, social media platforms, and search engines. With the increasing popularity of mobile browsing, many online ads are displayed with visual information in the form of a cover image in addition to text descriptions to grab the attention of users. Various recent studies have focused on predicting the click rates of online advertisements aware of visual features or composing optimal advertisement elements to enhance visibility. In this paper, we propose Advertisement Style Editing and Attractiveness Enhancement (AdSEE), which explores whether semantic editing to ads images can affect or alter the popularity of online advertisements. We introduce StyleGAN-based facial semantic editing and inversion to ads images and train a click rate predictor attributing GAN-based face latent representations in addition to traditional visual and textual features to click rates. Through a large collected dataset named QQ-AD, containing 20,527 online ads, we perform extensive offline tests to study how different semantic directions and their edit coefficients may impact click rates. We further design a Genetic Advertisement Editor to efficiently search for the optimal edit directions and intensity given an input ad cover image to enhance its projected click rates. Online A/B tests performed over a period of 5 days have verified the increased click-through rates of AdSEE-edited samples as compared to a control group of original ads, verifying the relation between image styles and ad popularity. We open source the code for AdSEE research at https://github.com/LiyaoJiang1998/adsee.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
