CTR-Driven Advertising Image Generation with Multimodal Large Language Models
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arXiv
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| Format: | Preprint |
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2025
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| author | Chen, Xingye Feng, Wei Du, Zhenbang Wang, Weizhen Chen, Yanyin Wang, Haohan Liu, Linkai Li, Yaoyu Zhao, Jinyuan Li, Yu Zhang, Zheng Lv, Jingjing Shen, Junjie Lin, Zhangang Shao, Jingping Shao, Yuanjie You, Xinge Gao, Changxin Sang, Nong |
| author_facet | Chen, Xingye Feng, Wei Du, Zhenbang Wang, Weizhen Chen, Yanyin Wang, Haohan Liu, Linkai Li, Yaoyu Zhao, Jinyuan Li, Yu Zhang, Zheng Lv, Jingjing Shen, Junjie Lin, Zhangang Shao, Jingping Shao, Yuanjie You, Xinge Gao, Changxin Sang, Nong |
| contents | In web data, advertising images are crucial for capturing user attention and improving advertising effectiveness. Most existing methods generate background for products primarily focus on the aesthetic quality, which may fail to achieve satisfactory online performance. To address this limitation, we explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective. Firstly, we build targeted pre-training tasks, and leverage a large-scale e-commerce multimodal dataset to equip MLLMs with initial capabilities for advertising image generation tasks. To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL), which can jointly utilize multimodal features and accurately reflect user click preferences. Meanwhile, a product-centric preference optimization strategy is developed to ensure that the generated background content aligns with the product characteristics after fine-tuning, enhancing the overall relevance and effectiveness of the advertising images. Extensive experiments have demonstrated that our method achieves state-of-the-art performance in both online and offline metrics. Our code and pre-trained models are publicly available at: https://github.com/Chenguoz/CAIG. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CTR-Driven Advertising Image Generation with Multimodal Large Language Models Chen, Xingye Feng, Wei Du, Zhenbang Wang, Weizhen Chen, Yanyin Wang, Haohan Liu, Linkai Li, Yaoyu Zhao, Jinyuan Li, Yu Zhang, Zheng Lv, Jingjing Shen, Junjie Lin, Zhangang Shao, Jingping Shao, Yuanjie You, Xinge Gao, Changxin Sang, Nong Machine Learning Computer Vision and Pattern Recognition Graphics Information Retrieval In web data, advertising images are crucial for capturing user attention and improving advertising effectiveness. Most existing methods generate background for products primarily focus on the aesthetic quality, which may fail to achieve satisfactory online performance. To address this limitation, we explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective. Firstly, we build targeted pre-training tasks, and leverage a large-scale e-commerce multimodal dataset to equip MLLMs with initial capabilities for advertising image generation tasks. To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL), which can jointly utilize multimodal features and accurately reflect user click preferences. Meanwhile, a product-centric preference optimization strategy is developed to ensure that the generated background content aligns with the product characteristics after fine-tuning, enhancing the overall relevance and effectiveness of the advertising images. Extensive experiments have demonstrated that our method achieves state-of-the-art performance in both online and offline metrics. Our code and pre-trained models are publicly available at: https://github.com/Chenguoz/CAIG. |
| title | CTR-Driven Advertising Image Generation with Multimodal Large Language Models |
| topic | Machine Learning Computer Vision and Pattern Recognition Graphics Information Retrieval |
| url | https://arxiv.org/abs/2502.06823 |