Interaction Representation

Interaction representation focuses on effectively capturing and modeling the relationships between interacting entities, whether they are pedestrians in a crowd, humans and objects in an image, or molecules in a chemical compound. Current research emphasizes developing sophisticated model architectures, including graph neural networks, diffusion models, and recurrent neural networks, to learn these interactions, often incorporating contextual information and causal relationships for improved accuracy and interpretability. These advancements are crucial for improving the performance of various applications, such as trajectory prediction, human-object interaction detection, and molecular property prediction, by enabling more nuanced and accurate modeling of complex systems. The ultimate goal is to create representations that are both computationally efficient and capable of capturing the underlying causal mechanisms driving interactions.