{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/merbench-a-unified-evaluation-benchmark-for","title":"MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition","arxiv_id":"2401.03429","date":"2024-01-07","proceeding":null,"authors":["Zheng Lian","Licai Sun","Yong Ren","Hao Gu","Haiyang Sun","Lan Chen","Bin Liu","Jianhua Tao"],"abstract":"Multimodal emotion recognition plays a crucial role in enhancing user experience in human-computer interaction. Over the past few decades, researchers have proposed a series of algorithms and achieved impressive progress. Although each method shows its superior performance, different methods lack a fair comparison due to inconsistencies in feature extractors, evaluation manners, and experimental settings. These inconsistencies severely hinder the development of this field. Therefore, we build MERBench, a unified evaluation benchmark for multimodal emotion recognition. We aim to reveal the contribution of some important techniques employed in previous works, such as feature selection, multimodal fusion, robustness analysis, fine-tuning, pre-training, etc. We hope this benchmark can provide clear and comprehensive guidance for follow-up researchers. Based on the evaluation results of MERBench, we further point out some promising research directions. Additionally, we introduce a new emotion dataset MER2023, focusing on the Chinese language environment. This dataset can serve as a benchmark dataset for research on multi-label learning, noise robustness, and semi-supervised learning. We encourage the follow-up researchers to evaluate their algorithms under the same experimental setup as MERBench for fair comparisons. Our code is available at: https://github.com/zeroQiaoba/MERTools.","url_abs":"https://arxiv.org/abs/2401.03429v3","url_pdf":"https://arxiv.org/pdf/2401.03429v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"merbench-a-unified-evaluation-benchmark-for","repo_url":"https://github.com/zeroqiaoba/mertools","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"merbench-a-unified-evaluation-benchmark-for","repo_url":"https://github.com/zeroqiaoba/mer2023-baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2401.03429","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}