In product advertising applications, the automated inpainting of backgrounds utilizing AI techniques in product images has emerged as a significant task. However, the techniques still suffer from issues such as inappropriate background and inconsistent product in generated product images, and existing approaches for evaluating the quality of generated product images are mostly inconsistent with human feedback causing the evaluation for this task to depend on manual annotation. To relieve the issues above, this paper proposes Human Feedback and Product Consistency (HFPC), which can automatically assess the generated product images based on two modules. Firstly, to solve inappropriate backgrounds, human feedback on 44,000 automated inpainting product images is collected to train a reward model based on multi-modal features extracted from BLIP and comparative learning. Secondly, to filter generated product images containing inconsistent products, a fine-tuned segmentation model is employed to segment the product of the original and generated product images and then compare the differences between the above two. Extensive experiments have demonstrated that HFPC can effectively evaluate the quality of generated product images and significantly reduce the expense of manual annotation. Moreover, HFPC achieves state-of-the-art(96.4% in precision) in comparison to other open-source visual-quality-assessment models. Dataset and code are available at: https://github.com/created-Bi/background inpainting products dataset/.

翻译：在产品广告应用中，利用人工智能技术对产品图像进行自动化背景修复已成为一项重要任务。然而，现有技术仍存在生成产品图像背景不恰当及产品不一致等问题，且当前评估生成图像质量的方法大多与人类反馈不一致，导致该任务的评估依赖人工标注。为缓解上述问题，本文提出基于人类反馈与产品一致性的评估框架，该框架通过两个模块自动评估生成的产品图像。首先，针对背景不恰当问题，我们收集了44,000张自动修复产品图像的人类反馈数据，基于BLIP提取的多模态特征与对比学习训练奖励模型。其次，为筛选包含不一致产品的生成图像，采用微调的分割模型对原始图像与生成图像中的产品进行分割，进而比较两者的差异。大量实验表明，该框架能有效评估生成产品图像的质量，并显著降低人工标注成本。相较于其他开源视觉质量评估模型，本框架达到了最先进的性能（精确度96.4%）。数据集与代码已公开于：https://github.com/created-Bi/background inpainting products dataset/。