Multi-Queue Momentum Contrast for microvideo-product retrieval

Yali Du, Yinwei Wei, Wei Ji, Fan Liu, Xin Luo, Liqiang Nie

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

6 Citations (Scopus)


The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their micro-videos, which not only provides them with business income but helps the audiences to discover their interesting products. However, due to the micro-video recording by unprofessional equipment, involving various topics and including multiple modalities, it is challenging to locate the products related to micro-videos efficiently, appropriately, and accurately. We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances. A novel approach named Multi-Queue Momentum Contrast (MQMC) network is proposed for bidirectional retrieval, consisting of the uni-modal feature and multi-modal instance representation learning. Moreover, a discriminative selection strategy with a multi-queue is used to distinguish the importance of different negatives based on their categories. We collect two large-scale microvideo-product datasets (MVS and MVS-large) for evaluation and manually construct the hierarchical category ontology, which covers sundry products in daily life. Extensive experiments show that MQMC outperforms the state-of-the-art baselines. Our replication package (including code, dataset, etc.) is publicly available at

Original languageEnglish
Title of host publicationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
EditorsLuo Si, Evimaria Terzi, Panayiotis Tsaparas
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450394079
Publication statusPublished - 2023
Externally publishedYes
EventACM International Conference on Web Search and Data Mining 2023 - Singapore, Singapore
Duration: 27 Feb 20233 Mar 2023
Conference number: 16th (Proceedings) (Website)


ConferenceACM International Conference on Web Search and Data Mining 2023
Abbreviated titleWSDM 2023
Internet address


  • datasets
  • microvideo-product
  • momentum contrast
  • multi-modal retrieval
  • multi-queue

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