Cognition-Driven Real-Time Personality Detection via Language-Guided Contrastive Visual Attention

Xiaoya Gao, Jingjing Wang, Shoushan Li, Guodong Zhou

Abstract: In this paper, we address a novel Cognition-driven Real-time Personality Detection (C-RPD) task, aiming to predict the personality trait (e.g., romantic and humorous) real-time shown by a human being from the perspective of cognitive psychology. Specifically, this task is motivated by a cognition difference phenomenon that humans with different personality traits tend to focus on different portions of the image and then give different personality-oriented language descriptions when observing an image. On this basis, we propose a new Language-guided Contrastive Visual Attention (L-CVA) approach to capture the above cognition difference information for addressing the C-RPD task. Experimental results on a real-world multimodal personality corpus verify the great advantage of our L-CVA approach to C-RPD over the state-of-the-art baselines. This justifies the importance of the cognition difference information to C-RPD and the effectiveness of our approach in capturing such information.