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Fine-Grained Cross-Modal Contrast Learning for Video-Text Retrieval

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Advanced Intelligent Computing Technology and Applications (ICIC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14866))

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Abstract

Video-sharing platforms emphasize video-text retrieval in multimodal information retrieval. Existing methods often overlook video text intricacies and redundancy, focusing mainly on single-granularity information. To address this, we propose Fine-grained Cross-modal Contrast Learning (FCCL), an end-to-end framework. FCCL includes a frame enhancement module to reduce data complexity by discerning key features from each video frame. Additionally, we introduce a multimodal attention model to identify text-similar video sub-regions accurately. We also intro-duce a multi-granularity discrepancy analysis model to capture cross-modal similarity across different levels, including video-sentence, frame-sentence, and frame-word perspectives. Experimental results on MSR-VTT and MSVD datasets demonstrate FCCL's superiority in video-text retrieval. Code is available at: https://github.com/LHlh917/FCCL.

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Disclosure of Interests

The authors have no competing interests to declare that are relevant to the content of this article.

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Acknowledgements

This work was supported in part by Anhui Provincial Key Research and Development Program (No. 2022a05020042), the University Synergy Innovation Program of Anhui Province (No. GXXT-2022-043), and Natural Science Research Project of Anhui Educational Committee (No. 2022AH051783).

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Correspondence to Fudong Nian .

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Liu, H., Lv, G., Gu, Y., Nian, F. (2024). Fine-Grained Cross-Modal Contrast Learning for Video-Text Retrieval. In: Huang, DS., Zhang, X., Guo, J. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14866. Springer, Singapore. https://doi.org/10.1007/978-981-97-5594-3_25

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  • DOI: https://doi.org/10.1007/978-981-97-5594-3_25

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  • Online ISBN: 978-981-97-5594-3

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