Summary of Mma-dfer: Multimodal Adaptation Of Unimodal Models For Dynamic Facial Expression Recognition In-the-wild, by Kateryna Chumachenko et al.
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild
by Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
First submitted to arxiv on: 13 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed method advances multimodal Dynamic Facial Expression Recognition (DFER) by adapting self-supervised learning (SSL)-pre-trained disjoint unimodal encoders. The approach focuses on intra-modality adaptation, cross-modal alignment, and temporal adaptation to improve DFER performance. Building upon recent advancements in multimodal emotion recognition, the method demonstrates state-of-the-art results on two popular DFER benchmarks, including DFEW and MFAW. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand people’s emotions better by improving facial expression recognition technology. It finds a new way to make this technology work well with different types of data, like audio and video. This can help create more human-like machines that understand how we feel. The method does well on two important tests for facial expression recognition. |
Keywords
» Artificial intelligence » Alignment » Self supervised
