MEGC2023: ACM Multimedia 2023 ME Grand Challenge
Davison, Adrian K.; Li, Jingting; Yap, Moi Hoon; See, John; Cheng, Wen-Huang; Li, Xiaobai; Hong, Xiaopeng; Wang, Su-Jing (2023-10-27)
Davison, Adrian K.
Li, Jingting
Yap, Moi Hoon
See, John
Cheng, Wen-Huang
Li, Xiaobai
Hong, Xiaopeng
Wang, Su-Jing
ACM
27.10.2023
Davison, Adrian K., et al. “MEGC2023: ACM Multimedia 2023 ME Grand Challenge.” Proceedings of the 31st ACM International Conference on Multimedia, ACM, 2023, pp. 9625–29. DOI.org (Crossref), https://doi.org/10.1145/3581783.3612833
https://creativecommons.org/licenses/by/4.0/
© 2023 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
© 2023 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202401051080
https://urn.fi/URN:NBN:fi:oulu-202401051080
Tiivistelmä
Abstract
Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment. Unfortunately, the small sample problem severely limits the automation of ME analysis. Furthermore, due to the weak and transient nature of MEs, it is difficult for models to distinguish it from other types of facial actions. Therefore, ME in long videos is a challenging task, and the current performance cannot meet the practical application requirements. Addressing these issues, this challenge focuses on ME and the macro-expression (MaE) spotting task. This year, in order to evaluate algorithms' performance more fairly, based on CAS(ME)2, SAMM Long Videos, SMIC-E-long, CAS(ME)3 and 4DME, we build an unseen cross-cultural long-video test set. All participating algorithms are required to run on this test set and submit their results on a leaderboard with a baseline result.
Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment. Unfortunately, the small sample problem severely limits the automation of ME analysis. Furthermore, due to the weak and transient nature of MEs, it is difficult for models to distinguish it from other types of facial actions. Therefore, ME in long videos is a challenging task, and the current performance cannot meet the practical application requirements. Addressing these issues, this challenge focuses on ME and the macro-expression (MaE) spotting task. This year, in order to evaluate algorithms' performance more fairly, based on CAS(ME)2, SAMM Long Videos, SMIC-E-long, CAS(ME)3 and 4DME, we build an unseen cross-cultural long-video test set. All participating algorithms are required to run on this test set and submit their results on a leaderboard with a baseline result.
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