2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CL2023★ 2 cited
Interpretable multimodal sentiment analysis based on textual modality descriptions by using large-scale language models
Sixia Li, Shogo Okada
Multimodal sentiment analysis is an important area for understanding the user's internal states. Deep learning methods were effective, but the problem of poor interpretability has…
cs.LG2023
Estimating Driver Personality Traits from On-Road Driving Data
Ryusei Kimura, Takahiro Tanaka, Yuki Yoshihara +3
This paper focuses on the estimation of a driver's psychological characteristics using driving data for driving assistance systems. Driving assistance systems that support drivers…
cs.HC2022
Personality-adapted multimodal dialogue system
Tamotsu Miyama, Shogo Okada
This paper describes a personality-adaptive multimodal dialogue system developed for the Dialogue Robot Competition 2022. To realize a dialogue system that adapts the dialogue stra…