Knowledge Commons of Institute of Automation,CAS
MCCN: Multimodal Coordinated Clustering Network for Large-Scale Cross-modal Retrieval | |
Zeng, Zhixiong1,2; Sun, Ying1,2; Mao, Wenji1,2 | |
2021-10 | |
会议名称 | ACM International Conference on Multimedia (ACM MM) |
会议日期 | Oct. 20-24, 2021 |
会议地点 | Virtual Event |
摘要 | Cross-modal retrieval is an important multimedia research area which aims to take one type of data as the query to retrieve relevant data of another type. Most of the existing methods follow the paradigm of pair-wise learning and class-level learning to generate a common embedding space, where the similarity of heterogeneous multimodal samples can be calculated. However, in contrast to large-scale cross-modal retrieval applications which often need to tackle multiple modalities, previous studies on cross-modal retrieval mainly focus on two modalities (i.e., text-image or text-video). In addition, for large-scale cross-modal retrieval with modality diversity, another important problem is that the available training data are considerably modality-imbalanced. In this paper, we focus on the challenging problem of modality-imbalanced cross-modal retrieval, and propose a Multimodal Coordinated Clustering Network (MCCN) which consists of two modules, Multimodal Coordinated Embedding (MCE) module to alleviate the imbalanced training data and Multimodal Contrastive Clustering (MCC) module to tackle the imbalanced optimization. The MCE module develops a data-driven approach to coordinate multiple modalities via multimodal semantic graph for the generation of modality-balanced training samples. The MCC module learns class prototypes as anchors to preserve the pair-wise and class-level similarities across modalities for intra-class compactness and inter-class separation, and further introduces intra-class and inter-class margins to enhance optimization flexibility. We conduct experiments on the benchmark multimodal datasets to verify the effectiveness of our proposed method. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48792 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Mao, Wenji |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zeng, Zhixiong,Sun, Ying,Mao, Wenji. MCCN: Multimodal Coordinated Clustering Network for Large-Scale Cross-modal Retrieval[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
3474085.3475670.pdf(1676KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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