AffectiveFusionNet: a multimodal emotion recognition using combination of Visual Transformers and Variational Autoencoders

AffectiveFusionNet showcases a new era in multimodal emotion recognition, ingeniously integrating the strengths of Visual Transformers (ViTs) and Variational Autoencoders (VAEs) with the advanced principles of COGMEN and V2EM. This state-of-the-art model is meticulously engineered to detect and decode intricate emotional cues from a combination of visual and conversational data, setting a new benchmark for precision in the field. ViTs are harnessed within AffectiveFusionNet to delve into the subtle emotional indicators present in visual inputs, capitalizing on their powerful self-attention mechanisms. Concurrently, VAEs are employed to encapsulate and regenerate the nuanced emotional content found in audio and textual data, ensuring a rich, multimodal emotional representation. The synergy of these technologies, along with the relational learning from COGMEN and the hierarchical attention from V2EM, positions AffectiveFusionNet at the forefront of emotion recognition which can be integrated into humanoid robots for emotional understanding of the subjects. Demonstrating superior performance on prominent datasets like IEMOCAP and CMU-MOSEI, AffectiveFusionNet not only pushes forward the capabilities of emotion detection systems but also paves the way for more perceptive and real-time emotional intelligence in artificial intelligence and robotic platforms. Future work aims to refine its real-time analytical prowess and adaptability to complex environments.