MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning

TL;DR

The MER 2023 challenge advances multimodal emotion recognition by focusing on multi-label classification, modality robustness under noise, and semi-supervised learning, providing a new benchmark dataset for the community.

Contribution

This paper introduces the first MER challenge with three tracks, a new benchmark dataset, and promotes research in multimodal emotion recognition, especially for Chinese data.

Findings

Successful organization of MER 2023 challenge with diverse tracks

Introduction of a high-quality multimodal emotion recognition dataset

Encouragement for future research using the dataset

Abstract

The first Multimodal Emotion Recognition Challenge (MER 2023) 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 Agreement and send it to our official email address merchallenge.contact@gmail.com. We believe this high-quality dataset can become a new benchmark in multimodal emotion recognition, especially for the Chinese research community.