Subjective Perception

Subjective perception research investigates how individuals experience and interpret sensory information, focusing on the discrepancies between objective measurements and individual experiences. Current research emphasizes quantifying these discrepancies across various domains, including multimodal data analysis (e.g., image captioning across languages), human-robot interaction (e.g., evaluating robot handover trajectories), and automated driving (e.g., integrating subjective driver feedback into objective performance metrics). This work often employs machine learning models, such as SVMs and large language models, to analyze subjective data and improve the accuracy of predictions based on subjective labels, aiming to bridge the gap between objective data and human experience in diverse applications.