Knowledge Commons of Institute of Automation,CAS
Efficient Token-Guided Image-Text Retrieval With Consistent Multimodal Contrastive Training | |
Liu, Chong1; Zhang, Yuqi2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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ISSN | 1057-7149 |
2023 | |
卷号 | 32页码:3622-3633 |
通讯作者 | Wang, Hongsong(hongsongwang@seu.edu.cn) ; Chen, Weihua(kugang.cwh@alibaba-inc.com) |
摘要 | Image-text retrieval is a central problem for understanding the semantic relationship between vision and language, and serves as the basis for various visual and language tasks. Most previous works either simply learn coarse-grained representations of the overall image and text, or elaborately establish the correspondence between image regions or pixels and text words. However, the close relations between coarse- and fine-grained representations for each modality are important for image-text retrieval but almost neglected. As a result, such previous works inevitably suffer from low retrieval accuracy or heavy computational cost. In this work, we address image-text retrieval from a novel perspective by combining coarse- and fine-grained representation learning into a unified framework. This framework is consistent with human cognition, as humans simultaneously pay attention to the entire sample and regional elements to understand the semantic content. To this end, a Token-Guided Dual Transformer (TGDT) architecture which consists of two homogeneous branches for image and text modalities, respectively, is proposed for image-text retrieval. The TGDT incorporates both coarse- and fine-grained retrievals into a unified framework and beneficially leverages the advantages of both retrieval approaches. A novel training objective called Consistent Multimodal Contrastive (CMC) loss is proposed accordingly to ensure the intra- and inter-modal semantic consistencies between images and texts in the common embedding space. Equipped with a two-stage inference method based on the mixed global and local cross-modal similarity, the proposed method achieves state-of-the-art retrieval performances with extremely low inference time when compared with representative recent approaches. Code is publicly available: github.com/LCFractal/TGDT. |
关键词 | Index Terms-Image-text retrieval multimodal transformer multimodal contrastive training |
DOI | 10.1109/TIP.2023.3286710 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Southeast University Start-Up Grant for New Faculty[RF1028623063] ; National Key Research and Development Program of China[2022ZD0117900] ; National Natural Science Foundation of China[62236010] ; National Natural Science Foundation of China[62276261] |
项目资助者 | Southeast University Start-Up Grant for New Faculty ; National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:001024111100002 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/53754 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Wang, Hongsong; Chen, Weihua |
作者单位 | 1.Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China 2.Alibaba Grp, Beijing 100102, Peoples R China 3.Southeast Univ, Dept Comp Sci & Engn, Nanjing 210096, Peoples R China 4.Chinese Acad Sci CASIA, Inst Automat, Ctr Res Intelligent Percept & Comp CRIPAC, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Liu, Chong,Zhang, Yuqi,Wang, Hongsong,et al. Efficient Token-Guided Image-Text Retrieval With Consistent Multimodal Contrastive Training[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:3622-3633. |
APA | Liu, Chong.,Zhang, Yuqi.,Wang, Hongsong.,Chen, Weihua.,Wang, Fan.,...&Wang, Liang.(2023).Efficient Token-Guided Image-Text Retrieval With Consistent Multimodal Contrastive Training.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,3622-3633. |
MLA | Liu, Chong,et al."Efficient Token-Guided Image-Text Retrieval With Consistent Multimodal Contrastive Training".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):3622-3633. |
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