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Open Access

KnowER: Knowledge enhancement for efficient text-video retrieval

School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
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Abstract

The widespread adoption of mobile Internet and the Internet of things (IoT) has led to a significant increase in the amount of video data. While video data are increasingly important, language and text remain the primary methods of interaction in everyday communication, text-based cross-modal retrieval has become a crucial demand in many applications. Most previous text-video retrieval works utilize implicit knowledge of pre-trained models such as contrastive language-image pre-training (CLIP) to boost retrieval performance. However, implicit knowledge only records the co-occurrence relationship existing in the data, and it cannot assist the model to understand specific words or scenes. Another type of out-of-domain knowledge—explicit knowledge—which is usually in the form of a knowledge graph, can play an auxiliary role in understanding the content of different modalities. Therefore, we study the application of external knowledge base in text-video retrieval model for the first time, and propose KnowER, a model based on knowledge enhancement for efficient text-video retrieval. The knowledge-enhanced model achieves state-of-the-art performance on three widely used text-video retrieval datasets, i.e., MSRVTT, DiDeMo, and MSVD.

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Intelligent and Converged Networks
Pages 93-105
Cite this article:
Kou H, Yang Y, Hua Y. KnowER: Knowledge enhancement for efficient text-video retrieval. Intelligent and Converged Networks, 2023, 4(2): 93-105. https://doi.org/10.23919/ICN.2023.0009

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Received: 03 April 2023
Revised: 04 May 2023
Accepted: 16 May 2023
Published: 30 June 2023
© All articles included in the journal are copyrighted to the ITU and TUP.

This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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