Spatio Temporal Scene Graph

Spatio-temporal scene graphs represent dynamic scenes as networks of objects and their evolving relationships over time, aiming to improve machine understanding of videos. Current research focuses on developing models that accurately anticipate future interactions, often employing graph neural networks (GNNs) and recurrent neural networks (RNNs) like LSTMs, sometimes integrated with differential equation solvers for continuous-time modeling. These advancements are significantly impacting fields like autonomous driving, video question answering, and medical applications such as epileptic seizure detection by enabling more robust and efficient analysis of complex visual data.