Recent advancements in generative artificial intelligence are driving a significant transformation in information retrieval and content generation, creating substantial opportunities for online advertising. Text-to-image generation technology has become increasingly prevalent in advertising content production, demonstrating promising performance improvements in terms of semantic relevance and visual appeal. However, existing models often suffer from inadequate representation of entity concepts, such as prominent product brands and recognizable landmarks. This inherent limitation subsequently leads to notable deficiencies in brand tonality, industry-specific relevance, and market adaptability of the generated advertising content. To address this challenge, we propose a multimodal ad content generation framework specifically engineered for online advertising system, particularly focused on resolving the deficiency in entity concepts. Our framework is comprised of two phases: first, an image captioning module with entity-aware learning based on multimodal large language model, leveraging retrieval-augmented techniques to incorporate entity concepts into image descriptions; second, a text-to-image diffusion model refined on image-text pairs enriched with entity concepts to facilitate entity-grounded image generation. Extensive experiments validate the effectiveness of our framework, demonstrating superior performance in both image captioning and image generation compared to existing methods, particularly in the accuracy of depiction of relevant entities in advertising images. Moreover, the deployment of the framework in the system primary traffic of Baidu Search Ads, has brought significant enhancements to advertisement revenue for both advertisers and the platform.

High-quality multimodal training data is of critical importance for improving of multimodal model performance. However, the utilization of web-crawled vision-caption pairs is hindered by the presence of noise and irrelevance, as well as a lack of Chinese data. Large Language Models (LLM) and Large Multimodal Models (LMM) has demonstrated promising performance in cross-modal understanding and generation. In light of this, we propose a Chinese visual captioning pipeline for the synthesis of high-quality data. Our pipeline is comprised of two phases: the initial training of an encoder for visual understanding; and the subsequent fine-tuning of a captioning model in a two-stage iterative human-in-the-loop process, where the captioning model incorporates the pre-trained vision encoder and LLM by a visual cross-attention querying transformer. Extensive experiments have been conducted to validate our framework, including both quantitative and qualitative evaluation of captions generated from images and videos. The synthesis pipeline has been integrated into the ad image creative generation process in Baidu Search Ads, resulting in enhanced capabilities in prompt following.

Dynamic image advertising is an add-on service in search advertising that matches visuals to search ads in real-time. However, the image matching system encompasses various sub-tasks with different objectives, increasing the complexity of achieving global optimization. Besides, prevalent long-tailed data poses a challenge to the multimodal representation learning in dynamic image advertising. Recently, vision-language pre-trained models have achieved remarkable performance across a variety of multimodal tasks, and implemented as the foundational representation model in electronic business scenarios. In this paper, to improve multimodal content understanding in Dynamic Image adVERtising, we present a viSion-language rEpresentation model (referred to as DIVERSE) that learns on cross-view and cross-token contrastive loss. Moreover, with large-scale curated advertising image-text data and extensive efficient training techniques, we scale DIVERSE to 12 billion parameters, which is the biggest Chinese multimodal representation model in industrial practices. Experiment results demonstrate the distinct advantages of DIVERSE12B in business datasets, with competitive performance on public benchmarks. Further evaluation in downstream applications including ad text-image retrieval, text-image relevance modeling, and image content moderation, shows that it outperforms previous separately-trained models across offline and online metrics. Moreover, DIVERSE12B has been implemented on the system primary traffic of Baidu Search Ads, bringing considerable increase to both user experience, and revenue for advertisers and search engine.

Recent advances in generative artificial intelligence have revolutionized information retrieval and content generation, opening up new opportunities for the e-commerce industry. In particular, text-to-image generation models offer a novel approach to guiding the image generation process using natural language input, which is inspiring for multimodal search advertising. Traditional multimodal search ads require advertisers to prepare ad creatives, such as ad images, which is time-consuming and requires uniform image specifications and content quality inspection. To this end, we propose a streamlined generation framework for search ad image creatives. First, we prepare a Chinese image caption model with high-quality image-caption pairs to bootstrap training data refinement. With curated high-quality images and synthesized descriptive captions, we then train a Chinese text-to-image generation model, the largest to date, using SDXL and a 10-billion multimodal text encoder. Specifically, we introduce a two-stage bilingual multimodal representation alignment process to seamlessly integrate the text encoder with the generation model. Extensive experiments validate the effectiveness of our framework, including assessments of image captioning and image generation. The implementation of our framework in Baidu Search Ads shows significant revenue increase, For example, beauty industry ads with generated image creatives achieve a 29% higher click-through rate (CTR).