Automatic image editing has great demands because of its numerous
applications, and the use of natural language instructions is essential to
achieving flexible and intuitive editing as the user imagines. A pioneering
work in text-driven image editing, StyleCLIP, finds an edit direction in the
CLIP space and then edits the image by mapping the direction to the StyleGAN
space. At the same time, it is difficult to tune appropriate inputs other than
the original image and text instructions for image editing. In this study, we
propose a method to construct the edit direction adaptively in the StyleGAN and
CLIP spaces with SVM. Our model represents the edit direction as a normal
vector in the CLIP space obtained by training a SVM to classify positive and
negative images. The images are retrieved from a large-scale image corpus,
originally used for pre-training StyleGAN, according to the CLIP similarity
between the images and the text instruction. We confirmed that our model
performed as well as the StyleCLIP baseline, whereas it allows simple inputs
without increasing the computational time.

我们提出了一种使用 SVM 在 StyleGAN 和 CLIP 空间自适应构建编辑方向的方法，将大规模图像语料库中与文本指令相似的图像通过 CLIP 相似性检索，用 SVM 训练正负图像分类器将编辑方向表示为 CLIP 空间中的法向量，并验证其性能与 StyleCLIP 基准一致，而且不会增加计算时间。

一种鲁棒的文本驱动图像编辑方法：自适应探索 StyleGAN 和 CLIP 潜空间中的方向

Robust Text-driven Image Editing Method that Adaptively Explores  Directions in Latent Spaces of StyleGAN and CLIP

Modeling what makes an advertisement persuasive, i.e., eliciting the desired
response from consumer, is critical to the study of propaganda, social
psychology, and marketing. Despite its importance, computational modeling of
persuasion in computer vision is still in its infancy, primarily due to the
lack of benchmark datasets that can provide persuasion-strategy labels
associated with ads. Motivated by persuasion literature in social psychology
and marketing, we introduce an extensive vocabulary of persuasion strategies
and build the first ad image corpus annotated with persuasion strategies. We
then formulate the task of persuasion strategy prediction with multi-modal
learning, where we design a multi-task attention fusion model that can leverage
other ad-understanding tasks to predict persuasion strategies. Further, we
conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500
companies where we use our model's predictions to analyze which strategies work
with different demographics (age and gender). The dataset also provides image
segmentation masks, which labels persuasion strategies in the corresponding ad
images on the test split. We publicly release our code and dataset
this https URL.

本文介绍了一种使用多模态学习的注意力融合模型来预测广告中运用的说服策略，并使用构建的广告图像语料库对模型的效果进行了测试。测试数据中的图像分割掩模标注了对应广告中的说服策略。通过对 30 所财富 500 强企业的 1600 个广告活动的实际案例研究，可以分析对不同人口统计学数据（年龄和性别）采用哪种策略更有效。