<ul><li>Dynamic Facial Expression Recognition (DFER) has received significant interest for enabling empathic and human-compatible technologies.</li><li>Multimodal emotion recognition based on audio and video data is being explored to improve DFER models' robustness towards real-world applications.</li><li>Advancements in self-supervised learning (SSL) and adapting pre-trained static models are being utilized in multimodal DFER.</li><li>This research work proposes adapting SSL-pre-trained disjoint unimodal encoders to improve multimodal DFER performance and achieves state-of-the-art results on DFER benchmarks.</li></ul>

Best Research on Facial Expression Recognition(FER) part4(Computer Vision + Artificial…

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