Event Representation

Event representation research focuses on developing effective methods to capture and utilize information from sequences of events, aiming to improve prediction accuracy and understanding of complex temporal dynamics across diverse domains. Current research emphasizes integrating large language models with temporal point processes or graph neural networks to leverage both semantic and temporal information, often employing contrastive learning and parameter-efficient fine-tuning techniques to enhance model performance and efficiency. These advancements are significantly impacting various fields, including video question answering, object recognition, and event-based visual perception, by enabling more accurate and robust systems for analyzing and predicting event sequences.