Abstract
With the increasing popularity of online videos, research on video corpus retrieval (VCR) has made significant progress. However, existing VCR models have not performed well in the medical field due to the unique characteristics of medical VCR task. Specifically, the open-ended queries used in medical VCR are more challenging compared to image-caption style queries, and the long duration of medical videos poses a great burden on model retrieval efficiency. To address these challenges, we propose a two-stage framework based on GPT-3.5 and cross-modal contrastive global-span (CCGS) for medical video VCR (termed GPT-CMR). In the first stage, we leverage the powerful natural language processing capabilities of the large language model (LLM) GPT-3.5 to improve retrieval efficiency. In the second stage, we use CCGS model to further enhance retrieval accuracy. Additionally, we developed a CCGS-VCR Analyzer to leverage the characteristics of the CCGS model’s output without additional training costs. According to the official result, our method achieve first place in Track 2 of the NLPCC 2023 Task 5 competition. Experiments show that our method has retrieval efficiency and accuracy far exceeding the official baseline.
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Acknowledgments
This work was supported by the Guangxi Natural Science Foundation (No. 2022GXNSFAA035627), Guangxi Natural Science Foundation Key Project (Application No. 2023JJD170015), National Natural Science Foundation of China (62276072), Guangxi Scientific and Technological Bases and Talents Special Projects (guikeAD23026213 and guikeAD23026230), Innovation Project of Guangxi Graduate Education, and the Open Research Fund of Guangxi Key Laboratory of Multimedia Communications and Network Technology.
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Lei, N. et al. (2023). A Two-Stage Chinese Medical Video Retrieval Framework with LLM. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14304. Springer, Cham. https://doi.org/10.1007/978-3-031-44699-3_19
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