Abstract
The fusion of multimodal cues, i.e., visual, audio, and language, can provide complementary insights and benefit sentiment analysis. However, not all of them are always available in practical scenarios, posing a challenge for inference with incomplete modality. One idea to address this issue is tailoring distillation algorithms to transfer multimodal knowledge from a full-modality teacher to the incomplete-modality student for missing information compensation. However, existing works utilize fixed and unified knowledge from the pretrained teacher to guide students under different missing states, ignoring their varying capacities. Consequently, the knowledge may be challenging for students with lower capacities to assimilate due to the huge gap. Thus, we propose a missing-customized distillation framework, to adapt the teacher’s knowledge to different missing-state students for better knowledge transfer. Specifically, for the student side, we devise a learnable missing token for each modality to perceive the current missing status. Then for the teacher side, these missing-aware tokens, along with extra trainable adapters, are inserted into it to facilitate knowledge adaptation. With a novel gradient-guided interactive training strategy, our method ensures that the teacher provides student-adaptive knowledge for different students and then the student absorbs valuable knowledge for improved missing information recovery. Extensive experiments on CMU-MOSI and CMU-MOSEI datasets validate the efficiency of our method compared with the state-of-the-arts across various missing rates.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Andrew, G., Arora, R., Bilmes, J., Livescu, K.: Deep canonical correlation analysis. In: International Conference on Machine Learning, pp. 1247–1255. PMLR (2013)
Degottex, G., Kane, J., Drugman, T., Raitio, T., Scherer, S.: Covarep—a collaborative voice analysis repository for speech technologies. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 960–964. IEEE (2014)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Du, Y., Czarnecki, W.M., Jayakumar, S.M., Farajtabar, M., Pascanu, R., Lakshminarayanan, B.: Adapting auxiliary losses using gradient similarity. arXiv preprint arXiv:1812.02224 (2018)
Ekman, P., Freisen, W.V., Ancoli, S.: Facial signs of emotional experience. J. Pers. Soc. Psychol. 39(6), 1125 (1980)
Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vision 129, 1789–1819 (2021)
Han, W., Chen, H., Kan, M.Y., Poria, S.: Mm-align: learning optimal transport-based alignment dynamics for fast and accurate inference on missing modality sequences. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 10498–10511 (2022)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
Komodakis, N., Zagoruyko, S.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. In: ICLR (2017)
Kornblith, S., Norouzi, M., Lee, H., Hinton, G.: Similarity of neural network representations revisited. In: International Conference on Machine Learning, pp. 3519–3529. PMLR (2019)
Lee, Y.L., Tsai, Y.H., Chiu, W.C., Lee, C.Y.: Multimodal prompting with missing modalities for visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14943–14952 (2023)
Li, S., Deng, W., Hu, J.: Momentum distillation improves multimodal sentiment analysis. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 423–435. Springer (2022)
Li, Y., Wang, Y., Cui, Z.: Decoupled multimodal distilling for emotion recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6631–6640 (2023)
Lian, Z., Chen, L., Sun, L., Liu, B., Tao, J.: Gcnet: graph completion network for incomplete multimodal learning in conversation. IEEE Trans. Pattern Anal. Mach. Intell. (2023)
Liang, P.P., Zadeh, A., Morency, L.P.: Foundations and recent trends in multimodal machine learning: Principles, challenges, and open questions. arXiv preprint arXiv:2209.03430 (2022)
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9), 1–35 (2023)
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)
Mirzadeh, S.I., Farajtabar, M., Li, A., Levine, N., Matsukawa, A., Ghasemzadeh, H.: Improved knowledge distillation via teacher assistant. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5191–5198 (2020)
Pham, H., Liang, P.P., Manzini, T., Morency, L.P., Póczos, B.: Found in translation: Learning robust joint representations by cyclic translations between modalities. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6892–6899 (2019)
Qiu, Y., Zhao, Z., Yao, H., Chen, D., Wang, Z.: Modal-aware visual prompting for incomplete multi-modal brain tumor segmentation. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 3228–3239 (2023)
Rao, J., Meng, X., Ding, L., Qi, S., Liu, X., Zhang, M., Tao, D.: Parameter-efficient and student-friendly knowledge distillation. IEEE Trans. Multimed. (2023)
Sun, T., Wei, Y., Ni, J., Liu, Z., Song, X., Wang, Y., Nie, L.: Muti-modal emotion recognition via hierarchical knowledge distillation. IEEE Trans. Multimed. (2024)
Tsai, Y.H.H., Bai, S., Liang, P.P., Kolter, J.Z., Morency, L.P., Salakhutdinov, R.: Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the Conference. Association for Computational Linguistics. Meeting, vol. 2019, p. 6558. NIH Public Access (2019)
Wang, W., Arora, R., Livescu, K., Bilmes, J.: On deep multi-view representation learning. In: International Conference on Machine Learning, pp. 1083–1092. PMLR (2015)
Wang, Y., Cui, Z., Li, Y.: Distribution-consistent modal recovering for incomplete multimodal learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 22025–22034 (2023)
Wei, S., Luo, C., Luo, Y.: Mmanet: Margin-aware distillation and modality-aware regularization for incomplete multimodal learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20039–20049 (2023)
Wei, S., Luo, Y., Ma, X., Ren, P., Luo, C.: Msh-net: Modality-shared hallucination with joint adaptation distillation for remote sensing image classification using missing modalities. IEEE Trans. Geosci. Remote Sensing (2023)
Xie, M., Han, Z., Zhang, C., Bai, Y., Hu, Q.: Exploring and exploiting uncertainty for incomplete multi-view classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19873–19882 (2023)
Xing, X., Chen, Z., Zhu, M., Hou, Y., Gao, Z., Yuan, Y.: Discrepancy and gradient-guided multi-modal knowledge distillation for pathological glioma grading. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 636–646. Springer (2022)
Zadeh, A., Zellers, R., Pincus, E., Morency, L.P.: Mosi: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos. arXiv preprint arXiv:1606.06259 (2016)
Zadeh, A.B., Liang, P.P., Poria, S., Cambria, E., Morency, L.P.: Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2236–2246 (2018)
Zeng, J., Liu, T., Zhou, J.: Tag-assisted multimodal sentiment analysis under uncertain missing modalities. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1545–1554 (2022)
Zhao, J., Li, R., Jin, Q.: Missing modality imagination network for emotion recognition with uncertain missing modalities. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 2608–2618 (2021)
Zhu, Y., Wang, Y.: Student customized knowledge distillation: bridging the gap between student and teacher. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5057–5066 (2021)
Acknowledgements
This work was supported in part by NSFC under Grant U2003207, in part by Jiangsu Frontier Technology Basic Research Project under Grant BK20192004.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hu, Z., Zheng, W., Wei, M., Shi, M., Zong, Y. (2025). Missing Customized Distillation Network for Incomplete Multimodal Sentiment Analysis. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15308. Springer, Cham. https://doi.org/10.1007/978-3-031-78186-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-78186-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-78185-8
Online ISBN: 978-3-031-78186-5
eBook Packages: Computer ScienceComputer Science (R0)
