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Articulate Similarity: A Visual Search Framework For Embodied Knowledge In A Confucian Rites Video Archive

Authors: Hou, Yumeng; Seydou, Fadel; Cian, David;

Articulate Similarity: A Visual Search Framework For Embodied Knowledge In A Confucian Rites Video Archive

Abstract

{"references": ["Tsuchida, Shuhei, Satoru Fukayama, and Masataka Goto. \"Query-by-dancing: a dance music retrieval system based on body-motion similarity.\" International Conference on Multimedia Modeling. Springer, Cham, 2019.", "Cao, Zhe, et al. \"OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields.\" IEEE transactions on pattern analysis and machine intelligence 43.1 (2019): 172-186.", "Lin Peng. 2020. Why is re-enactment critical in the research of Confucian Rites.", "Kenderdine, Sarah, and Jeffrey Shaw. \"Archives in Motion.\" Museum and Archive on the Move: Changing Cultural Institutions in the Digital Era, edited by Oliver Grau Wendy Coones and Viola R\u00fchse (2017): 211-233."]}

Poster presented at 16th International Digital Curation Conference (IDCC21), on "Data quality and data limitations: working towards equality through data curation", 19 April 2021, Edinburgh, UK By Yumeng Hou, Fadel Mamar Seydou and David Cian.

Keywords

embodied knowledge, machine learning, computational archive, FAIR

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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