MEGC2023: ACM Multimedia 2023 ME Grand Challenge

Davison, Adrian K ORCID: https://orcid.org/0000-0002-6496-0209, Li, Jingting ORCID: https://orcid.org/0000-0001-8742-8488, Yap, Moi Hoon ORCID: https://orcid.org/0000-0001-7681-4287, See, John ORCID: https://orcid.org/0000-0003-3005-4109, Cheng, Wen-Huang ORCID: https://orcid.org/0000-0002-4662-7875, Li, Xiaobai ORCID: https://orcid.org/0000-0003-4519-7823, Hong, Xiaopeng ORCID: https://orcid.org/0000-0002-0611-0636 and Wang, Su-Jing ORCID: https://orcid.org/0000-0002-8774-6328 (2023) MEGC2023: ACM Multimedia 2023 ME Grand Challenge. In: MM '23: The 31st ACM International Conference on Multimedia, 29 October 2023- 3 November 2023, Ottawa, Canada.

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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.