short-paper

Enhancing Dynamic Image Advertising with Vision-Language Pre-training

Authors:

Lin LiuAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3310 - 3314

https://doi.org/10.1145/3539618.3591844

Published: 18 July 2023 Publication History

Abstract

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.

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Cited By

Zhao XZhao KJin ZYang YTao WChen XHan CLi SLiu LSerra ESpezzano F(2024)Scaling Vison-Language Foundation Model to 12 Billion Parameters in Baidu Dynamic Image AdvertisingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680014(5102-5110)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680014
Yang YZhao XZhao KJin ZTao WLiu LLi SSerra ESpezzano F(2024)Multi-Stage Refined Visual Captioning for Baidu Ad Creatives GenerationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679969(4198-4202)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679969
Zhao KZhao XJin ZYang YTao WHan CLi SLiu LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Enhancing Baidu Multimodal Advertisement with Chinese Text-to-Image Generation via Bilingual Alignment and Caption SynthesisProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661350(2855-2859)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661350
Show More Cited By

Index Terms

Enhancing Dynamic Image Advertising with Vision-Language Pre-training
1. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Image search

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 18 July 2023

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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Zhao XZhao KJin ZYang YTao WChen XHan CLi SLiu LSerra ESpezzano F(2024)Scaling Vison-Language Foundation Model to 12 Billion Parameters in Baidu Dynamic Image AdvertisingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680014(5102-5110)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680014
Yang YZhao XZhao KJin ZTao WLiu LLi SSerra ESpezzano F(2024)Multi-Stage Refined Visual Captioning for Baidu Ad Creatives GenerationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679969(4198-4202)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679969
Zhao KZhao XJin ZYang YTao WHan CLi SLiu LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Enhancing Baidu Multimodal Advertisement with Chinese Text-to-Image Generation via Bilingual Alignment and Caption SynthesisProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661350(2855-2859)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661350
Wen HSong XChen XWei YNie LChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657727(229-239)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657727
Wan YLi WWu XXu JYang JJin CHe LSong MWang R(2023)Automatic Image Aesthetic Assessment for Human-designed Digital ImagesProceedings of the 1st International Workshop on Multimedia Content Generation and Evaluation: New Methods and Practice10.1145/3607541.3616810(1-8)Online publication date: 29-Oct-2023
https://dl.acm.org/doi/10.1145/3607541.3616810

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