TY - GEN
T1 - MER 2023
T2 - 31st ACM International Conference on Multimedia, MM 2023
AU - Lian, Zheng
AU - Sun, Haiyang
AU - Sun, Licai
AU - Chen, Kang
AU - Xu, Mngyu
AU - Wang, Kexin
AU - Xu, Ke
AU - He, Yu
AU - Li, Ying
AU - Zhao, Jinming
AU - Liu, Ye
AU - Liu, Bin
AU - Yi, Jiangyan
AU - Wang, Meng
AU - Cambria, Erik
AU - Zhao, Guoying
AU - Schuller, Björn W.
AU - Tao, Jianhua
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/10/26
Y1 - 2023/10/26
N2 - The first Multimodal Emotion Recognition Challenge (MER 2023)1 was successfully held at ACM Multimedia. The challenge focuses on system robustness and consists of three distinct tracks: (1) MER-MULTI, where participants are required to recognize both discrete and dimensional emotions; (2) MER-NOISE, in which noise is added to test videos for modality robustness evaluation; (3) MER-SEMI, which provides a large amount of unlabeled samples for semi-supervised learning. In this paper, we introduce the motivation behind this challenge, describe the benchmark dataset, and provide some statistics about participants. To continue using this dataset after MER 2023, please sign a new End User License Agreement2 and send it to our official email address3. We believe this high-quality dataset can become a new benchmark in multimodal emotion recognition, especially for the Chinese research community.
AB - The first Multimodal Emotion Recognition Challenge (MER 2023)1 was successfully held at ACM Multimedia. The challenge focuses on system robustness and consists of three distinct tracks: (1) MER-MULTI, where participants are required to recognize both discrete and dimensional emotions; (2) MER-NOISE, in which noise is added to test videos for modality robustness evaluation; (3) MER-SEMI, which provides a large amount of unlabeled samples for semi-supervised learning. In this paper, we introduce the motivation behind this challenge, describe the benchmark dataset, and provide some statistics about participants. To continue using this dataset after MER 2023, please sign a new End User License Agreement2 and send it to our official email address3. We believe this high-quality dataset can become a new benchmark in multimodal emotion recognition, especially for the Chinese research community.
KW - modality robustness
KW - multi-label learning
KW - multimodal emotion recognition challenge (mer 2023)
KW - semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85179550237&partnerID=8YFLogxK
U2 - 10.1145/3581783.3612836
DO - 10.1145/3581783.3612836
M3 - Conference contribution
AN - SCOPUS:85179550237
T3 - MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia
SP - 9610
EP - 9614
BT - MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia
PB - Association for Computing Machinery, Inc
Y2 - 29 October 2023 through 3 November 2023
ER -