Interval Logic Tensor Networks

Event detection (ED) from sequences of data is a critical challenge in various fields, including surveillance [Clavel et al., 2005], multimedia processing [Xiang and Wang, 2019, Lai, 2022], and social network analysis [Cordeiro and Gama, 2016]. Neural network-based architectures have been developed for ED, leveraging various data types such as text, images, social media data, and audio. Integrating commonsense and structural knowledge about events and their relationships can significantly enhance machine learning methods for ED. For example, in analyzing a soccer match video, the knowledge that a red card shown to a player is typically followed by the player leaving the field can aid in event detection. Additionally, knowledge about how simple events compose complex events is also useful for complex event detection. Background knowledge has been shown to improve the detection of complex events especially when training data is limited [Yin et al., 2020].