TRN (Temporal Relation Networks)

Temporal Relation Networks (TRN) are machine learning models designed to understand and analyze sequences of events or actions over time. They focus on capturing the relationships between different moments within a video or time series, such as how actions occur relative to each other. By modeling these temporal dependencies, TRNs enhance the ability of computers to recognize complex activities, like sports plays or daily routines, more accurately. Essentially, TRNs help AI systems interpret not just what is happening, but how events unfold and relate over time, leading to better understanding of dynamic sequential data.