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Personality computing and affective computing have gained recent interest inmany research areas. The datasets for the task generally have multiplemodalities like video, audio, language and bio-signals. In this paper, wepropose a flexible model for the task which exploits all available data. Thetask involves complex relations and to avoid using a large model for videoprocessing specifically, we propose the use of behaviour encoding which boostsperformance with minimal change to the model. Cross-attention usingtransformers has become popular in recent times and is utilised for fusion ofdifferent modalities. Since long term relations may exist, breaking the inputinto chunks is not desirable, thus the proposed model processes the entireinput together. Our experiments show the importance of each of the abovecontributions

Multimodal Personality Recognition using Cross-Attention Transformer and Behaviour Encoding