A Knowledge-Based Hierarchical Causal Inference Network for Video Action Recognition

Currently, existing action recognition methods mainly use a data-driven method to extract spatio-temporal representations of actions for recognition. However, this method may face performance bottlenecks. At the same time, existing action recognition methods are easily affected by the bias of scene information and object information in videos. In order to explore the essential causal relationship between factors and remove bias in action recognition, we introduce the theory of causal inference into the field of action recognition and propose a Knowledge-based Hierarchical Causal Inference Network (KHCIN) to help us step toward a new direction of inference in action recognition. First, we construct a Knowledge-based Hierarchical Causal Graph (KHCG) to structurally represent the scene, object and motion knowledge of a video. Then, in the model inference stage, we perform factual causal inference on a video on the constructed KHCG, and then deploy counterfactual inference on the Direct Content Hierarchy (DCH) and Indirect Interaction Hierarchy (IIH) in the KHCG. For DCH, we intervene in the model at the decision level to highlight bias errors in the model predictions. For the IIH, we focus on intervening in the feature modelling process. The biased interactions are revealed by interrupting the information communication in the feature space. By comparing the results of factual and counterfactual inference, we can easily expose the biased information in the original representations and eliminate them. Driven by counterfactual causal inference, our approach can significantly improve the performance of action recognition while improving model explainability. Extensive experiments demonstrate the effectiveness of this method. We hope that KHCIN can provide some new ideas for better introduction of causal inference theory in the action recognition community in the future.