HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding

Visual interactivity understanding within visual scenes presents a significant challenge in computer vision. Existing methods focus on complex interactivities while leveraging a simple relationship model. These methods however struggle with a diversity of appearance situation position interaction and relation in videos. This limitation hinders the ability to fully comprehend the interplay within the complex visual dynamics of subjects. In this paper we delve into interactivities understanding within visual content by deriving scene graph representations from dense interactivities among humans and objects. To achieve this goal we first present a new dataset containing Appearance-Situation-Position-Interaction-Relation predicates named ASPIRe offering an extensive collection of videos marked by a wide range of interactivities. Then we propose a new approach named Hierarchical Interlacement Graph (HIG) which leverages a unified layer and graph within a hierarchical structure to provide deep insights into scene changes across five distinct tasks. Our approach demonstrates superior performance to other methods through extensive experiments conducted in various scenarios.