Knowledge-Guided Short-Context Action Anticipation in Human-Centric Videos

This allows us to predict future actions accurately, video understanding, including video production and editing particularly from short-horizon observations - a key aspect [20, 41, 6, 38]. This work focuses on anticipating actions that prior works [11, 1, 22, 34, 2, 17] in action anticipation from short video segments and provides potential avenues fail to cater to. to enhance the editing process. In particular, the ability In our work, we utilize Knowledge Graphs (KG) to capture to extract actions from a video segment can be utilized the relationship between entities present in the video in two manners: 1) It allows for intelligent clip suggestions and link them to their respective affordances and the potential for future editing, namely the ability to suggest videos given tools that could be used to afford them in a particular what will likely happen next, and 2) it provides information way. Prior work [40, 26, 23, 15] has introduced efficient on what generally would happen, which allows editors to methods of identifying such relationships, which can refine their composition to either confirm or contradict a subsequently be utilized to identify the potential for certain viewer's expectation.