Mining Intention with Heterogeneous Attention and Distillation for Interaction Anticipation

Short-term Object Interaction Anticipation STA aims to enhance intelligent systems with predictive capabilities and decision-making support by forecasting future interactions between objects. Existing methods often rely on visual changes in short video inputs, which limits the depth and accuracy of motivation prediction. In this study, we propose a novel approach, termed IntenFormer, inspired by how humans make decisions by understanding intentions. Specifically, IntenFormer employs a heterogeneous attention mechanism to simultaneously mine long- and short-term information frameworks, while incorporating knowledge distillation by utilizing a pretrained global intention model as a teacher, enabling the model to learn intention patterns. Extensive experiments on the Ego4D-STA dataset demonstrate that IntenFormer achieves highly competitive results, underscoring the efficacy of a unified approach to intention prediction and knowledge distillation.