@inproceedings{lin-etal-2026-missmac,
title = "{M}iss{MAC}-Bench: Building Solid Benchmark for Missing Modality Issue in Robust Multimodal Affective Computing",
author = "Lin, Ronghao and
Lu, Honghao and
Wu, Ruixing and
Xiong, Aolin and
Chu, Qinggong and
He, Qiaolin and
Mai, Sijie and
Hu, Haifeng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.978/",
doi = "10.18653/2026.findings-emnlp.978",
pages = "20669--20684",
ISBN = "979-8-89176-462-0",
abstract = "Current Multimodal Affective Computing (MAC) heavily rely on the completeness of multiple modalities to accurately understand human{'}s affective state. However, in real-world scenarios, the availability of modality data is often dynamic and uncertain, leading to substantial performance fluctuations due to the distribution shifts and semantic deficiencies of the incomplete multimodal inputs. Known as the missing modality issue, this challenge poses a critical barrier to the robustness and practical deployment of MAC models. To systematically quantify this issue, we introduce \textbf{MissMAC-Bench}, a comprehensive benchmark designed to establish fair and unified evaluation standards from the perspective of cross-modal synergy. Two guiding principles are proposed, including no missing prior during training, and one single model capable of handling both complete and incomplete modality scenarios, thereby ensuring better generalization. Moreover, to bridge the gap between academic research and real-world applications, our benchmark integrates evaluation protocols with both fixed and random missing patterns at the dataset and instance levels. Extensive experiments conducted on 3 widely-used language models across 4 datasets validate the effectiveness of diverse MAC approaches in tackling the missing modality issue. Our benchmark provides a solid foundation for advancing robust MAC and promotes the development of multimedia data mining. Our code is released in \url{https://github.com/RH-Lin/MissMAC-Bench}."
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<abstract>Current Multimodal Affective Computing (MAC) heavily rely on the completeness of multiple modalities to accurately understand human’s affective state. However, in real-world scenarios, the availability of modality data is often dynamic and uncertain, leading to substantial performance fluctuations due to the distribution shifts and semantic deficiencies of the incomplete multimodal inputs. Known as the missing modality issue, this challenge poses a critical barrier to the robustness and practical deployment of MAC models. To systematically quantify this issue, we introduce MissMAC-Bench, a comprehensive benchmark designed to establish fair and unified evaluation standards from the perspective of cross-modal synergy. Two guiding principles are proposed, including no missing prior during training, and one single model capable of handling both complete and incomplete modality scenarios, thereby ensuring better generalization. Moreover, to bridge the gap between academic research and real-world applications, our benchmark integrates evaluation protocols with both fixed and random missing patterns at the dataset and instance levels. Extensive experiments conducted on 3 widely-used language models across 4 datasets validate the effectiveness of diverse MAC approaches in tackling the missing modality issue. Our benchmark provides a solid foundation for advancing robust MAC and promotes the development of multimedia data mining. Our code is released in https://github.com/RH-Lin/MissMAC-Bench.</abstract>
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%0 Conference Proceedings
%T MissMAC-Bench: Building Solid Benchmark for Missing Modality Issue in Robust Multimodal Affective Computing
%A Lin, Ronghao
%A Lu, Honghao
%A Wu, Ruixing
%A Xiong, Aolin
%A Chu, Qinggong
%A He, Qiaolin
%A Mai, Sijie
%A Hu, Haifeng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lin-etal-2026-missmac
%X Current Multimodal Affective Computing (MAC) heavily rely on the completeness of multiple modalities to accurately understand human’s affective state. However, in real-world scenarios, the availability of modality data is often dynamic and uncertain, leading to substantial performance fluctuations due to the distribution shifts and semantic deficiencies of the incomplete multimodal inputs. Known as the missing modality issue, this challenge poses a critical barrier to the robustness and practical deployment of MAC models. To systematically quantify this issue, we introduce MissMAC-Bench, a comprehensive benchmark designed to establish fair and unified evaluation standards from the perspective of cross-modal synergy. Two guiding principles are proposed, including no missing prior during training, and one single model capable of handling both complete and incomplete modality scenarios, thereby ensuring better generalization. Moreover, to bridge the gap between academic research and real-world applications, our benchmark integrates evaluation protocols with both fixed and random missing patterns at the dataset and instance levels. Extensive experiments conducted on 3 widely-used language models across 4 datasets validate the effectiveness of diverse MAC approaches in tackling the missing modality issue. Our benchmark provides a solid foundation for advancing robust MAC and promotes the development of multimedia data mining. Our code is released in https://github.com/RH-Lin/MissMAC-Bench.
%R 10.18653/2026.findings-emnlp.978
%U https://aclanthology.org/2026.findings-emnlp.978/
%U https://doi.org/10.18653/2026.findings-emnlp.978
%P 20669-20684
Markdown (Informal)
[MissMAC-Bench: Building Solid Benchmark for Missing Modality Issue in Robust Multimodal Affective Computing](https://aclanthology.org/2026.findings-emnlp.978/) (Lin et al., Findings 2026)
ACL
- Ronghao Lin, Honghao Lu, Ruixing Wu, Aolin Xiong, Qinggong Chu, Qiaolin He, Sijie Mai, and Haifeng Hu. 2026. MissMAC-Bench: Building Solid Benchmark for Missing Modality Issue in Robust Multimodal Affective Computing. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20669–20684, Budapest, Hungary. Association for Computational Linguistics.