The rapid advancement of video generation technology, particularly in the domain of image-to-video generation, is significantly transforming both personal and industrial applications. The performance of video generation models is heavily influenced by the quality of data, specifically video content and captions. Among these, the quality of video captions directly impacts the model's ability to follow user instructions. Currently, video captions are typically generated using vision-language model (VLM)-based methods. However, issues such as hallucinations, incomplete descriptions, and inaccuracies remain prevalent. To address these challenges, we propose a novel optimization framework for a multi-expert, structured video captioning model, specifically designed to enhance the performance of Chinese image-to-video tasks in Baidu's business scenarios. First, we construct a structured video caption dataset by combining a large language model (LLM) with human annotations, and introduce a hallucination-aware adaptive GRPO algorithm. A two-stage fine-tuning alignment strategy is employed to improve the accuracy of fine-grained content descriptions and mitigate hallucinations. Second, we design independent video sub-expert models to characterize subject motion intensity and camera movement, thereby expanding the dimensionality of video captions and addressing the issue of inaccurate sub-dimension descriptions in a single VLM. Finally, we leverage multi-level structured video captions to guide the training of the video generation model, resulting in improved quality and consistency in video generation. Extensive experiments demonstrate that our method consistently outperforms the baseline. Specifically, it achieves a 2.49% improvement in F1-score on event-level cross-validation and reduces hallucination occurrences by 30.9%. Moreover, our framework significantly enhances the quality of Chinese video generation, yielding a 2.9% increase in the VBench total score and a 9.2% improvement in the proportion of high-quality generated videos.

Recent advances in generative artificial intelligence have revolutionized information retrieval and content generation, opening up new opportunities for the e-commerce industry. Alignment learning between small models and parallel corpora cannot meet current needs. The success of ChatGPT demonstrates that large models need to first establish a fundamental understanding, and then utilize high-quality corpora for generation. Having a large model foundation is indispensable. In this paper, we establish a fundamental 10B multimodal model foundation for multimodal generation tasks and propose a scene-based alignment learning approach called conditional sample supervised fine-tuning for downstream generation tasks. Meanwhile, diffusion models are known to be vulnerable to outliers in training data. To address this, we utilize an alternative diffusion loss function that preserves the high quality of generated data like the original squared L2 loss while being robust to outliers.In practical test sets, the multimodal foundation fully demonstrates its alignment and comprehension abilities for graphic and textual content. Additionally, conditional fine-tuning and the design of the loss function significantly enhance the quality of generated content. The quality rate of images has increased by 34.3 percentage points, and prompt control has improved by 19.8 percentage points. The application of our framework in Baidu Search Ads has led to significant revenue growth. For instance, ads with generated image creatives have achieved a 29% higher click-through rate (CTR), resulting in a daily consumption of 3 million yuan.

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).

In commercial systems, the interaction between user behavior and advertising content forms a complex graph network that exhibits multi-scenario and multi-modal characteristics. Multi-scenario refers to the fact that ads are typically displayed in different scenarios on the same platform, such as Baidu App’s news feed and short video scenes. Across different scenarios, users’ interests have both commonalities and differences. Multi-modal refers to the fact that the influence of advertising content modality on users varies, and users’ sensitivity to different content modalities may also differ. We believe that the fundamental relationship between user behavior and advertising content may be multi-layered, influenced by the types of presenting content scenarios and the unique features of each modality. For example, in the news feed scenario, the importance of video titles may be different from that in the video stream scenario, where the visual content of the video may be more prominent. We propose a novel heterogeneous graph neural network method that integrates multi-scenario domains and multi-modal content features, which we call the Domain-Aware Multi-Modal graph model (DAMM-GCN). Our model achieves significant improvements in multiple scenarios compared with the traditional flat graph models such as Metapath2vec [1], GraphSAGE [2] and MMGCN [3]. Furthermore, our model has been deployed in the Baidu advertising system, obtaining 2.08% improvement on CPM (Cost Per Mille).

In the multimedia era, image becomes an effective medium in search advertising. Dynamic Image Advertising (DIA), a system that matches queries with appropriate ad images and generates multimodal ads, is introduced to improve user experience and ad revenue. The core of DIA is a query-image matching module performing ad image retrieval and relevance modeling. Current query-image matching suffers from data scarcity and inconsistency, and insufficient cross-modal fusion. Also, the retrieval and relevance models are separately trained, affecting overall performance. In this paper, we propose a vision-language framework for query-image matching. It consists of two parts. First, we design a base model combining different encoders and tasks, and train it on large-scale image-text pairs to learn general multimodal representation. Then, we fine-tune the base model on advertising business data, unifying relevance modeling and retrieval through multi-objective learning. Our framework has been implemented in Baidu search advertising system "Phoneix Nest". Online evaluation shows that it improves cost per mille (CPM) and click-through rate (CTR) by 1.04% and 1.865% on the system main traffic.

Recently, video BERT based on cross-modal attention has achieved excellent performance in many cross-modal tasks in academia. Nevertheless, the expensive computation cost of cross-modal attention makes video BERT impractical for large-scale search in industrial applications. Inspired by the success of the tree-based deep model (TDM) in the recommendation system, we present a enhanced video BERT (EVB). It provides a practical solution to deploy the heavy video BERT for the large-scale query-to-video search. The proposed EVB overcomes the limitation of TDM relying on global features, and makes the tree structure based on a global feature compatible with version BERT using a set of local features. What’s more, we proposes a similarity-based dynamic construction to integrate the optimization of model efficiency. The proposed EVB has been deployed in our video advertising platform and brings a considerable boost in CVR and CTR for advertisers.

Existing advertisements click-through rate (CTR) prediction models are mainly dependent on behavior ID features, which are learned based on the historical user-ad interactions. Nevertheless, behavior ID features relying on historical user behaviors are not feasible to describe new ads without previous interactions with users. To overcome the limitations of behavior ID features in modeling new ads, we exploit the visual content in ads to boost the performance of CTR prediction models. Specifically, we map each ad into a set of visual IDs based on its visual content. These visual IDs are further used for generating the visual embedding for enhancing CTR prediction models. We formulate the learning of visual IDs into a supervised quantization problem. Due to a lack of class labels for commercial images in advertisements, we exploit image textual descriptions as the supervision to optimize the image extractor for generating effective visual IDs. Meanwhile, since the hard quantization is non-differentiable, we soften the quantization operation to make it support the end-to-end network training. After mapping each image into visual IDs, we learn the embedding for each visual ID based on the historical user-ad interactions accumulated in the past. Since the visual ID embedding depends only on the visual content, it generalizes well to new ads. Meanwhile, the visual ID embedding complements the ad behavior ID embedding. Thus, it can considerably boost the performance of the CTR prediction models previously relying on behavior ID features for both new ads and ads that have accumulated rich user behaviors. After incorporating the visual ID embedding in the CTR prediction model of Baidu online advertising, the average CTR of ads improves by 1.46%, and the total charge increases by 1.10%.

Visual content in multimedia advertising effectively attracts the customer's attention. Search-based multimedia advertising is a cross-modal retrieval problem. Due to the modal gap between texts and images/videos, cross-modal image/video retrieval is a challenging problem. Recently, multi-modal dictionary BERT has bridged the model gap by unifying the images/videos and texts from different modalities through a multi-modal dictionary. In this work, we improve the multi-modal dictionary BERT by developing a multi-scale multi-modal dictionary and propose a Multi-scale Multi-modal Dictionary BERT (M^2D-BERT). The multi-scale dictionary partitions the feature space into different levels and is effective in describing the fine-level relevance and the coarse-level relevance between the text and images. Meanwhile, we constrain that the code-words in dictionaries from different scales to be orthogonal to each other. Thus, it ensures multiple dictionaries are complementary to each other. Moreover, we adopt a two-level residual quantization to enhance the capacity of each multi-modal dictionary. Systematic experiments conducted on large-scale cross-modal retrieval datasets demonstrate the excellent performance of our M2D-BERT.

Review rating prediction is of much importance for sentiment analysis and business intelligence. Existing methods work well when aspect-opinion pairs can be accurately extracted from review texts and aspect ratings are complete. The challenges of improving prediction accuracy are how to capture the semantics of review content and how to fill in the missing values of aspect ratings. In this paper, we propose a novel review rating prediction method, which improves the prediction accuracy by capturing deep semantics of review content and alleviating data missing problem of aspect ratings. The method firstly learns the latent vector representation of review content using skip-thought vectors, a state-of-the-art deep learning method, then, the missing values of aspect ratings are filled in based on users? history reviewing behaviors, finally, a novel optimization framework is proposed to predict the review rating. Experimental results on two real-world datasets demonstrate the efficacy of the proposed method.