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Developing models that remain robust under in-the-wild variability is a key motivation for DFER research and its practical applications. Improving models through multimodal learning, which leverages audio and video data for richer, complementary representations, is one promising direction. However, existing methods still rely heavily on modality-specific encoders and coarse-grained content-level alignment, which hinders their ability to capture fine-grained emotional semantics and dynamic cross-modal interactions. To address this, we adopt parameter-efficient fine-tuning (PEFT) to facilitate audio-visual interaction. This strategy offers key advantages: (1) freezing parameters preserves upstream pretrained knowledge, ensuring that the model focuses solely on learning modules for audio-visual interaction and modal fusion; (2) a Mamba Fusion Adapter (MFAdapter) is inserted at each encoder layer to perform causal, audio-conditioned fusion over a frame-aligned token sequence, enabling efficient multi-level cross-modal injection with linear complexity; and (3) a Frame-level Feature Arrangement (FFA) strategy is introduced as a deterministic index-based arrangement scheme that arranges audio tokens at the video-frame rate, providing a frame-indexed temporal prior that supports the causal scan in MFAdapter; FFA reduces, but does not eliminate, coarse segment-level mismatch and is not claimed as verified frame-level synchronization. Notably, our method achieves competitive performance on DFEW and MAFW while updating 2.7% (4.7 million) of the model parameters, with the best WAR of 58.70% on MAFW among all compared methods and 76.62%\/65.25% (WAR\/UAR) on DFEW under the official five-fold cross-validation protocols.<\/jats:p>","DOI":"10.3390\/s26175384","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T10:59:23Z","timestamp":1787741963000},"page":"5384","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parameter-Efficient Audio-Visual Dynamic Facial Expression Recognition with Mamba Fusion Adapters and Frame-Level Feature Arrangement"],"prefix":"10.3390","volume":"26","author":[{"given":"Kangbo","family":"Ning","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Air Force Medical University, Xi\u2019an 710032, China"},{"name":"Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, No. 169 Changle West Road, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Shandong University of Finance and Economics, Jinan 250014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0630-3339","authenticated-orcid":false,"given":"Zhaoqiang","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9746-0032","authenticated-orcid":false,"given":"Dong","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Air Force Medical University, Xi\u2019an 710032, China"},{"name":"Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, No. 169 Changle West Road, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4498-6126","authenticated-orcid":false,"given":"Lei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Shandong University of Finance and Economics, Jinan 250014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"ref_1","unstructured":"Wang, Y., Yan, S., Liu, Y., Song, W., Liu, J., Chang, Y., Mai, X., Hu, X., Zhang, W., and Gan, Z. (2024). A survey on facial expression recognition of static and dynamic emotions. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"eaao6760","DOI":"10.1126\/scirobotics.aao6760","article-title":"Personalized machine learning for robot perception of affect and engagement in autism therapy","volume":"3","author":"Rudovic","year":"2018","journal-title":"Sci. Robot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1109\/TAFFC.2022.3188390","article-title":"Classifying emotions and engagement in online learning based on a single facial expression recognition neural network","volume":"13","author":"Savchenko","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/TAFFC.2016.2628787","article-title":"Predicting personalized image emotion perceptions in social networks","volume":"9","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TAFFC.2020.2981446","article-title":"Deep facial expression recognition: A survey","volume":"13","author":"Li","year":"2020","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.aej.2023.01.017","article-title":"A comprehensive survey on deep facial expression recognition: Challenges, applications, and future guidelines","volume":"68","author":"Sajjad","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jiang, X., Zong, Y., Zheng, W., Tang, C., Xia, W., Lu, C., and Liu, J. (2020, January 12\u201316). Dfew: A large-scale database for recognizing dynamic facial expressions in the wild. Proceedings of the 28th ACM International Conference on Multimedia, Virtual.","DOI":"10.1145\/3394171.3413620"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, Y., Dai, W., Feng, C., Wang, W., Yin, G., Zeng, J., and Shan, S. (2022). MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild. Proceedings of the 30th ACM International Conference on Multimedia (MM\u201922), ACM.","DOI":"10.1145\/3503161.3548190"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109368","DOI":"10.1016\/j.patcog.2023.109368","article-title":"Expression snippet transformer for robust video-based facial expression recognition","volume":"138","author":"Liu","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_10","unstructured":"Sun, L., Lian, Z., Liu, B., and Tao, J. (November, January 29). MAE-DFER: Efficient Masked Autoencoder for Self-Supervised Dynamic Facial Expression Recognition. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhao, Z., and Liu, Q. (2021, January 20\u201324). Former-dfer: Dynamic facial expression recognition transformer. Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, China.","DOI":"10.1145\/3474085.3475292"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, M.Z., Zha, Q., and Wu, H. (July, January 30). Soften the Mask: Adaptive Temporal Soft Mask for Efficient Dynamic Facial Expression Recognition. Proceedings of the 2025 IEEE International Conference on Multimedia and Expo (ICME), Nantes, France.","DOI":"10.1109\/ICME59968.2025.11209787"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Qu, L., Shi, H., Gao, D., Zheng, Y., and Li, T. (2025). From coarse to nuanced: Cross-modal alignment of Fine-grained linguistic cues and visual salient regions for dynamic emotion recognition. arXiv.","DOI":"10.1109\/TAFFC.2026.3722468"},{"key":"ref_14","unstructured":"Zhao, Z., and Patras, I. (2023, January 20\u201324). Prompting Visual-Language Models for Dynamic Facial Expression Recognition. Proceedings of the British Machine Vision Conference (BMVC), Aberdeen, UK."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102382","DOI":"10.1016\/j.inffus.2024.102382","article-title":"Hicmae: Hierarchical contrastive masked autoencoder for self-supervised audio-visual emotion recognition","volume":"108","author":"Sun","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, T., Liu, J., Cho, K., Ott, M., Liu, B., Glass, J., and Peng, F. (2021). Analyzing the forgetting problem in pretrain-finetuning of open-domain dialogue response models. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.eacl-main.95"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","unstructured":"Goodfellow, I.J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y. (2013). An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3192","DOI":"10.1109\/TCSVT.2023.3312858","article-title":"Transformer-based multimodal emotional perception for dynamic facial expression recognition in the wild","volume":"34","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, L., Lian, Z., Tao, J., Liu, B., and Niu, M. (2020, January 16). Multi-modal continuous dimensional emotion recognition using recurrent neural network and self-attention mechanism. Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-Life Media Challenge and Workshop, Seattle, WA, USA.","DOI":"10.1145\/3423327.3423672"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Y., Song, W., Gao, S., Huang, Y., Chen, Z., Ge, W., and Zhang, W. (2022, January 10\u201314). Dpcnet: Dual path multi-excitation collaborative network for facial expression representation learning in videos. Proceedings of the 30th ACM International Conference on Multimedia, Lisbon, Portugal.","DOI":"10.1145\/3503161.3547865"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, S., Kim, S., Park, J., and Sohn, K. (2019, January 27\u201328). Context-aware emotion recognition networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.01024"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"119301","DOI":"10.1016\/j.ins.2023.119301","article-title":"HiT-MST: Dynamic facial expression recognition with hierarchical transformers and multi-scale spatiotemporal aggregation","volume":"644","author":"Xia","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.ins.2022.03.062","article-title":"Clip-aware expressive feature learning for video-based facial expression recognition","volume":"598","author":"Liu","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_25","unstructured":"Li, H., Niu, H., Zhu, Z., and Zhao, F. (2023, January 7\u201314). Intensity-aware loss for dynamic facial expression recognition in the wild. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA."},{"key":"ref_26","unstructured":"Tao, Z., Wang, Y., Chen, Z., Wang, B., Yan, S., Jiang, K., Gao, S., and Zhang, W. (November, January 29). Freq-hd: An interpretable frequency-based high-dynamics affective clip selection method for in-the-wild facial expression recognition in videos. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kawamura, R., Hayashi, H., Takemura, N., and Nagahara, H. (2024, January 3\u20138). MIDAS: Mixing ambiguous data with soft labels for dynamic facial expression recognition. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii.","DOI":"10.1109\/WACV57701.2024.00642"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13507","DOI":"10.1109\/TCSVT.2024.3450652","article-title":"CFAN-SDA: Coarse-Fine Aware Network with Static-Dynamic Adaptation for Facial Expression Recognition in Videos","volume":"34","author":"Chen","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TAFFC.2016.2593719","article-title":"Facial expression recognition in video with multiple feature fusion","volume":"9","author":"Chen","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_30","unstructured":"Chen, H., Huang, H., Dong, J., Zheng, M., and Shao, D. (November, January 28). Finecliper: Multi-modal fine-grained clip for dynamic facial expression recognition with adapters. Proceedings of the 32nd ACM International Conference on Multimedia, Melbourne, Australia."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cheng, H., Zhao, Z., He, Y., Hu, Z., Li, J., Wang, M., and Hong, R. (2025, January 27\u201331). Vaemo: Efficient representation learning for visual-audio emotion with knowledge injection. Proceedings of the 33rd ACM International Conference on Multimedia, Dublin, Ireland.","DOI":"10.1145\/3746027.3754924"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5146","DOI":"10.1109\/TCSVT.2025.3619205","article-title":"Feature Evaluation and Joint Interaction for Audio-Visual Emotion Recognition","volume":"36","author":"Li","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chumachenko, K., Iosifidis, A., and Gabbouj, M. (2024, January 17\u201318). MMA-DFER: MultiModal Adaptation of Unimodal Models for Dynamic Facial Expression Recognition In-the-wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW63382.2024.00470"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TAFFC.2025.3618639","article-title":"Hybrid-Supervised Hypergraph-Enhanced Transformer for Micro-Gesture Based Emotion Recognition","volume":"17","author":"Xia","year":"2026","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_35","unstructured":"Narayanan, V., Sai, B., Ahuja, R., Likhar, P., Kumar, V.R., and Yogamani, S. (2026). MambaFusion: Adaptive State-Space Fusion for Multimodal 3D Object Detection. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"16664","DOI":"10.52202\/068431-1212","article-title":"Adaptformer: Adapting vision transformers for scalable visual recognition","volume":"35","author":"Chen","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jia, M., Tang, L., Chen, B.C., Cardie, C., Belongie, S., Hariharan, B., and Lim, S.N. (2022, January 23\u201327). Visual prompt tuning. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"ref_38","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2022, January 25\u201329). LoRA: Low-Rank Adaptation of Large Language Models. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_39","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., and Bhosale, S. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, H., Niu, H., Zhu, Z., and Zhao, F. (2023). Cliper: A unified vision-language framework for in-the-wild facial expression recognition. arXiv.","DOI":"10.1109\/ICME57554.2024.10687508"},{"key":"ref_41","unstructured":"Yoshimura, M., Hayashi, T., and Maeda, Y. (2025, January 24\u201328). MambaPEFT: Exploring parameter-efficient fine-tuning for mamba. Proceedings of the International Conference on Learning Representations, Singapore."},{"key":"ref_42","unstructured":"Li, S., Singh, H., and Grover, A. (October, January 29). Mamba-nd: Selective state space modeling for multi-dimensional data. Proceedings of the European Conference on Computer Vision, Milan, Italy."},{"key":"ref_43","unstructured":"Gu, A., and Dao, T. (2024, January 7\u20139). Mamba: Linear-time sequence modeling with selective state spaces. Proceedings of the First Conference on Language Modeling, Philadelphia, PA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., and Girshick, R. (2022, January 18\u201324). Masked autoencoders are scalable vision learners. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref_45","unstructured":"Huang, P.Y., Xu, H., Li, J., Baevski, A., Auli, M., Galuba, W., Metze, F., and Feichtenhofer, C. (December, January 28). Masked Autoencoders that Listen. Proceedings of the NeurIPS, New Orleans, LA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ma, B., Zhang, W., Qiu, F., and Ding, Y. (2023, January 17\u201324). A unified approach to facial affect analysis: The mae-face visual representation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00630"},{"key":"ref_47","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 4). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the ICLR, Vienna, Austria."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., and Ritter, M. (2017, January 5\u20139). Audio Set: An ontology and human-labeled dataset for audio events. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"ref_49","first-page":"12449","article-title":"wav2vec 2.0: A framework for self-supervised learning of speech representations","volume":"33","author":"Baevski","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3451","DOI":"10.1109\/TASLP.2021.3122291","article-title":"Hubert: Self-supervised speech representation learning by masked prediction of hidden units","volume":"29","author":"Hsu","year":"2021","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1109\/JSTSP.2022.3188113","article-title":"Wavlm: Large-scale self-supervised pre-training for full stack speech processing","volume":"16","author":"Chen","year":"2022","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., and Paluri, M. (2018, January 18\u201323). A closer look at spatiotemporal convolutions for action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00675"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yoon, S., Dey, S., Lee, H., and Jung, K. (2020, January 4\u20138). Attentive modality hopping mechanism for speech emotion recognition. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Virtual.","DOI":"10.1109\/ICASSP40776.2020.9054229"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gowda, S.N., Gao, B., and Clifton, D.A. (2024, January 27\u201331). Fe-adapter: Adapting image-based emotion classifiers to videos. Proceedings of the 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), Istanbul, Turkey.","DOI":"10.1109\/FG59268.2024.10581905"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., and Satoh, Y. (2017, January 22\u201329). Learning spatio-temporal features with 3d residual networks for action recognition. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.373"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TBIOM.2025.3546279","article-title":"Robust Dynamic Facial Expression Recognition","volume":"7","author":"Liu","year":"2025","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_58","unstructured":"Ma, F., Sun, B., and Li, S. (2022). Spatio-temporal transformer for dynamic facial expression recognition in the wild. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, B., Wu, S., Shen, S., Liu, F., Ding, S., and Zhou, A. (2023, January 17\u201324). Rethinking the learning paradigm for dynamic facial expression recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01722"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lee, B., Shin, H., Ku, B., and Ko, H. (2023, January 17\u201324). Frame level emotion guided dynamic facial expression recognition with emotion grouping. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00602"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"106573","DOI":"10.1016\/j.neunet.2024.106573","article-title":"CDGT: Constructing diverse graph transformers for emotion recognition from facial videos","volume":"179","author":"Chen","year":"2024","journal-title":"Neural Netw."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"130020","DOI":"10.1016\/j.neucom.2025.130020","article-title":"Dynamic facial expression recognition in the wild via Multi-Snippet Spatiotemporal Learning","volume":"636","author":"Zhang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1109\/TAFFC.2024.3436913","article-title":"Svfap: Self-supervised video facial affect perceiver","volume":"16","author":"Sun","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/TAFFC.2024.3453443","article-title":"From static to dynamic: Adapting landmark-aware image models for facial expression recognition in videos","volume":"16","author":"Chen","year":"2024","journal-title":"IEEE Trans. Affect. 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