Paper Submission
The challenge participants will be invited to submit a workshop-style paper describing their ML solutions and results on the dataset -- these will be peer-reviewed and once accepted, will appear in the IEEE FG 2024 Challenge/Workshop Proceedings. The format of the paper follows the same requirements as the main conference of the FG 2024.
Format: FG 2024 Challenges will consider Short and Long Papers format.
Short paper: 4 pages + 1 page for references
Long paper: 8 pages (+ any number of references)
The main difference between the two is in the size of contributions and not in their importance or technical quality. In other words, a long paper is expected to have a greater contribution than a short paper. More specifically:
Long papers (8 pages excluding references) should present original reports of substantive new research techniques, findings, and applications. They should place the work within the field and clearly indicate innovative aspects. Research procedures and technical methods should be presented in sufficient detail to ensure scrutiny and reproducibility. Results should be clearly communicated and implications of the contributions/findings for FG and beyond should be explicitly discussed.
Short papers (4 pages + 1 page for references) should present original and highly promising research or applications. Merit will be assessed in terms of originality, importance, and technical quality more so than scope and maturity of the work.
Submission site: https://easychair.org/conferences/?conf=react2024
Submission deadline: March 29, 2024
References:
Submissions should cite the following papers:
Theory paper and baseline paper:
[1] Song, S., Spitale, M., Luo, Y., Bal, B., and Gunes, H. "Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?." arXiv preprint arXiv:2302.06514 (2023).
[2] Song, Siyang, Micol Spitale, Cheng Luo, Cristina Palmero, German Barquero, Hengde Zhu, Sergio Escalera et al. "REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge." arXiv preprint arXiv:2401.05166 (2024).
[3] Song, Siyang, Micol Spitale, Cheng Luo, Germán Barquero, Cristina Palmero, Sergio Escalera, Michel Valstar et al. "React2023: The first multiple appropriate facial reaction generation challenge." In Proceedings of the 31st ACM International Conference on Multimedia, pp. 9620-9624. 2023.
Dataset papers:
[4] Ringeval, F., Sonderegger, A., Sauer, J., & Lalanne, D. (2013, April). Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions. In 2013 10th IEEE international conference and workshops on automatic face and gesture recognition (FG) (pp. 1-8). IEEE.
[5] Cafaro, A., Wagner, J., Baur, T., Dermouche, S., Torres Torres, M., Pelachaud, C., ... & Valstar, M. (2017, November). The NoXi database: multimodal recordings of mediated novice-expert interactions. In Proceedings of the 19th ACM International Conference on Multimodal Interaction (pp. 350-359).
Annotation and basic feature extraction tools:
[6] Song, Siyang, Yuxin Song, Cheng Luo, Zhiyuan Song, Selim Kuzucu, Xi Jia, Zhijiang Guo, Weicheng Xie, Linlin Shen, and Hatice Gunes. "GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features." arXiv preprint arXiv:2211.12482 (2022).
[7] Luo, Cheng, Siyang Song, Weicheng Xie, Linlin Shen, and Hatice Gunes. (2022, July) "Learning multi-dimensional edge feature-based au relation graph for facial action unit recognition." Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 1239-1246).
[8] Toisoul, Antoine, Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, and Maja Pantic. "Estimation of continuous valence and arousal levels from faces in naturalistic conditions." Nature Machine Intelligence 3, no. 1 (2021): 42-50.
[9] Eyben, Florian, Martin Wöllmer, and Björn Schuller. "Opensmile: the munich versatile and fast open-source audio feature extractor." In Proceedings of the 18th ACM international conference on Multimedia, pp. 1459-1462. 2010.
Submissions are encouraged to cite previous facial reaction generation papers:
[1] Huang, Yuchi, and Saad M. Khan. "Dyadgan: Generating facial expressions in dyadic interactions." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 11-18. 2017.
[2] Huang, Yuchi, and Saad Khan. "A generative approach for dynamically varying photorealistic facial expressions in human-agent interactions." In Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 437-445. 2018.
[3] Shao, Zilong, Siyang Song, Shashank Jaiswal, Linlin Shen, Michel Valstar, and Hatice Gunes. "Personality recognition by modelling person-specific cognitive processes using graph representation." In proceedings of the 29th ACM international conference on multimedia, pp. 357-366. 2021.
[4] Song, Siyang, Zilong Shao, Shashank Jaiswal, Linlin Shen, Michel Valstar, and Hatice Gunes. "Learning Person-specific Cognition from Facial Reactions for Automatic Personality Recognition." IEEE Transactions on Affective Computing (2022).
[5] Barquero, German, Sergio Escalera, and Cristina Palmero. "Belfusion: Latent diffusion for behavior-driven human motion prediction." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2317-2327. 2023.
[6] Zhou, Mohan, Yalong Bai, Wei Zhang, Ting Yao, Tiejun Zhao, and Tao Mei. "Responsive listening head generation: a benchmark dataset and baseline." In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVIII, pp. 124-142. Cham: Springer Nature Switzerland, 2022.
[7] Luo, Cheng, Siyang Song, Weicheng Xie, Micol Spitale, Linlin Shen, and Hatice Gunes. "ReactFace: Multiple Appropriate Facial Reaction Generation in Dyadic Interactions." arXiv preprint arXiv:2305.15748 (2023).
[8] Xu, Tong, Micol Spitale, Hao Tang, Lu Liu, Hatice Gunes, and Siyang Song. "Reversible Graph Neural Network-based Reaction Distribution Learning for Multiple Appropriate Facial Reactions Generation." arXiv preprint arXiv:2305.15270 (2023).
[9] Liang, Cong, Jiahe Wang, Haofan Zhang, Bing Tang, Junshan Huang, Shangfei Wang, and Xiaoping Chen. "Unifarn: Unified transformer for facial reaction generation." In Proceedings of the 31st ACM International Conference on Multimedia, pp. 9506-9510. 2023.
[10] Yu, Jun, Ji Zhao, Guochen Xie, Fengxin Chen, Ye Yu, Liang Peng, Minglei Li, and Zonghong Dai. "Leveraging the latent diffusion models for offline facial multiple appropriate reactions generation." In Proceedings of the 31st ACM International Conference on Multimedia, pp. 9561-9565. 2023.
[11] Hoque, Ximi, Adamay Mann, Gulshan Sharma, and Abhinav Dhall. "BEAMER: Behavioral Encoder to Generate Multiple Appropriate Facial Reactions." In Proceedings of the 31st ACM International Conference on Multimedia, pp. 9536-9540. 2023.