Summary of Multimodal Fusion with Pre-trained Model Features in Affective Behaviour Analysis In-the-wild, by Zhuofan Wen et al.
Multimodal Fusion with Pre-Trained Model Features in Affective Behaviour Analysis In-the-wild
by Zhuofan Wen, Fengyu Zhang, Siyuan Zhang, Haiyang Sun, Mingyu Xu, Licai Sun, Zheng Lian, Bin Liu, Jianhua Tao
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for Expression (Expr) Recognition and Valence-Arousal (VA) Estimation by combining multimodal fusion methods with pre-trained model features. The authors leverage large pre-trained models, such as those from the Aff-Wild2 database, to extract final hidden layers as features. These features are then preprocessed, interpolated or convolved to align them for modal fusion using different models. The proposed approach achieves outstanding performance in multimodal tasks and is evaluated on the Aff-Wild2 database. This work showcases the potential of combining multimodal fusion methods with pre-trained model features to tackle challenging expression recognition and valence-arousal estimation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to recognize how people express themselves and how they feel. It uses special computer models that are already very good at doing this, and combines them with other techniques to make it even better. The authors test their method on a big dataset of videos and sounds, and show that it can do a great job of recognizing expressions and emotions. This research could help us build more accurate machines that understand human feelings. |
