$M^3$T: Multi-Modal Continuous Valence-Arousal Estimation in the Wild

J. Deng, J. Guo, Y. Zhou, J. Yu, I. Kotsia, and S. Zafeiriou, “RetinaFace: Single-stage dense face localisation in the wild,” CoRR , vol. abs/1905.00641, 2019. [Online]. Available: http://arxiv.org/abs/1905.00641

Original

1905

Earlier work this paper cites.

J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” in Annual Conference of the International Speech Communication Association , B. Yegnanarayana, Ed. ISCA, 2018, pp. 1086–1090. [Online]. Available: https://doi.org/10.21437/Interspeech.2018-1929

1929

Earlier work this paper cites.

E. Friesen and P. Ekman, “Facial action coding system: a technique for the measurement of facial movement,” Palo Alto , vol. 3, 1978

1978

Earlier work this paper cites.

D. Kollias, M. A. Nicolaou, I. Kotsia, G. Zhao, and S. Zafeiriou, “Recognition of affect in the wild using deep neural networks,” in EEE Conference on Computer Vision and Pattern Recognition Workshops , 2017, pp. 1972–1979

1979

Earlier work this paper cites.

J. A. Russell, “A circumplex model of affect.” Journal of personality and social psychology , vol. 39, no. 6, p. 1161, 1980

1980

Earlier work this paper cites.

S. Zafeiriou, D. Kollias, M. A. Nicolaou, A. Papaioannou, G. Zhao, and I. Kotsia, “Aff-Wild: Valence and arousal ’in-the-wild’ challenge,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2017, pp. 1980–1987

1987

Earlier work this paper cites.

P. Ekman, “An argument for basic emotions,” Cognition & emotion , vol. 6, no. 3-4, pp. 169–200, 1992

1992

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations , Y. Bengio and Y. LeCun, Eds., 2015. [Online]. Available: http://arxiv.org/abs/1412.6980

Original

2015

Earlier work this paper cites.

S. Li, W. Deng, and J. Du, “Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild,” in IEEE Conference on Computer Vision and Pattern Recognition . IEEE Computer Society, 2017, pp. 2584–2593

2017

Cited alongside, same era.

A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: A large-scale speaker identification dataset,” in Annual Conference of the International Speech Communication Association , 2017, pp. 2616–2620

2017

Cited alongside, same era.

L. N. Smith, “Cyclical learning rates for training neural networks,” in IEEE Winter Conference on Applications of Computer Vision . IEEE Computer Society, 2017, pp. 464–472. [Online]. Available: https://doi.org/10.1109/WACV.2017.58

2017

Cited alongside, same era.

D. Kollias and S. Zafeiriou, “A multi-task learning & generation framework: Valence-arousal, action units & primary expressions,” CoRR , vol. abs/1811.07771, 2018. [Online]. Available: http://arxiv.org/abs/1811.07771

Original

2018

D. Kollias, P. Tzirakis, M. A. Nicolaou, A. Papaioannou, G. Zhao, B. W. 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 , vol. 127, no. 6-7, pp. 907–929, 2019

2019

Later among the works it cites.

A. Mollahosseini, B. Hassani, and M. H. Mahoor, “Affectnet: A database for facial expression, valence, and arousal computing in the wild,” IEEE Trans. Affective Computing , vol. 10, no. 1, pp. 18–31, 2019

2019

Later among the works it cites.

Y. Li, J. Zeng, S. Shan, and X. Chen, “Self-supervised representation learning from videos for facial action unit detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 924–10 933

2019

Later among the works it cites.

T. Makino, H. Liao, Y. M. Assael, B. Shillingford, B. Garcia, O. Braga, and O. Siohan, “Recurrent neural network transducer for audio-visual speech recognition,” CoRR , vol. abs/1911.04890, 2019

Original