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We propose a cross-modal co-attention model for continuous emotion recognition using visual-audio-linguistic information.
S. Zafeiriou, D. Kollias, M. A. Nicolaou, A. Papaioannou, G. Zhao, and I. Kotsia, “Aff-wild: Valence and arousal ‘in-the-wild’challenge,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on . IEEE, 2017, pp. 1980–1987
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M. Valstar, J. Gratch, B. Schuller, F. Ringeval, D. Lalanne, M. Torres Torres, S. Scherer, G. Stratou, R. Cowie, and M. Pantic, “Avec 2016: Depression, mood, and emotion recognition workshop and challenge,” in Proceedings of the 6th international workshop on audio/visual emotion challenge , 2016, pp. 3–10
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F. Ringeval, B. Schuller, M. Valstar, J. Gratch, R. Cowie, S. Scherer, S. Mozgai, N. Cummins, M. Schmitt, and M. Pantic, “Avec 2017: Real-life depression, and affect recognition workshop and challenge,” in Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge , 2017, pp. 3–9
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F. Ringeval, B. Schuller, M. Valstar, R. Cowie, H. Kaya, M. Schmitt, S. Amiriparian, N. Cummins, D. Lalanne, A. Michaud et al. , “Avec 2018 workshop and challenge: Bipolar disorder and cross-cultural affect recognition,” in Proceedings of the 2018 on audio/visual emotion challenge and workshop , 2018, pp. 3–13
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2019
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2019
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D. Kollias, P. Tzirakis, M. A. Nicolaou, A. Papaioannou, G. Zhao, B. Schuller, I. Kotsia, and S. Zafeiriou, “Deep affect prediction in-the-wild: Aff-wild database and challenge, deep architectures, and beyond,” International Journal of Computer Vision , pp. 1–23, 2019
2021
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2021
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S. Zhang, Y. Ding, Z. Wei, and C. Guan, “Continuous emotion recognition with audio-visual leader-follower attentive fusion,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3567–3574
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L. Stappen, L. Schumann, B. Sertolli, A. Baird, B. Weigell, E. Cambria, and B. W. Schuller, “Muse-toolbox: The multimodal sentiment analysis continuous annotation fusion and discrete class transformation toolbox,” in Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge , 2021, pp. 75–82
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2019
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A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
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F. Ringeval, B. Schuller, M. Valstar, N. Cummins, R. Cowie, L. Tavabi, M. Schmitt, S. Alisamir, S. Amiriparian, E.-M. Messner et al. , “Avec 2019 workshop and challenge: state-of-mind, detecting depression with ai, and cross-cultural affect recognition,” in Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop , 2019, pp. 3–12
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D. Kollias, A. Schulc, E. Hajiyev, and S. Zafeiriou, “Analysing affective behavior in the first abaw 2020 competition,” in 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)(FG) , pp. 794–800
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L. Sun, Z. Lian, J. Tao, B. Liu, and M. Niu, “Multi-modal continuous dimensional emotion recognition using recurrent neural network and self-attention mechanism,” in Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop , 2020, pp. 27–34
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D. Kollias and S. Zafeiriou, “Analysing affective behavior in the second abaw2 competition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3652–3660
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2021
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2022
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2022
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2022
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