CASIA OpenIR  > 多模态人工智能系统全国重点实验室
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
Zheng Lian1; Haiyang Sun2; Licai Sun2; Kang Chen3; Mingyu Xu1; Kexin Wang1; Ke Xu2; Yu He2; Ying Li4; Jinming Zhao5; Ye Liu6; Bin Liu1; Jiangyan Yi1; Meng Wang7; Erik Cambria8; Guoying Zhao9; Björn W. Schuller10; Jianhua Tao11
2023
Conference NameACM Multimedia
Conference DateOctober 29-November 3, 2023
Conference PlaceOttawa, ON, Canada
Abstract

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) MERMULTI, 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 semisupervised 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.

Sub direction classification智能交互
planning direction of the national heavy laboratory人机混合智能
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57084
Collection多模态人工智能系统全国重点实验室
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Peking University
4.Shandong Normal University
5.Renmin University of China
6.Institute of Psychology, CAS
7.Ant Group
8.Nanyang Technological University
9.University of Oulu
10.Imperial College London
11.Tsinghua University
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zheng Lian,Haiyang Sun,Licai Sun,et al. MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning[C],2023.
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