Prior Knowledge-driven Dynamic Scene Graph Generation with Causal Inference

The task of dynamic scene graph generation (DSGG) aims at constructing a set of frame-level scene graphs for the given video. It suffers from two kinds of spurious correlation problems. First, the spurious correlation between input object pair and predicate label is caused by the biased predicate sample distribution in dataset. Second, the spurious correlation between contextual information and predicate label arises from interference caused by background content in both the current frame and adjacent frames of the video sequence. To alleviate spurious correlations, our work is formulated into two sub-tasks: video-specific commonsense graph generation (VsCG) and causal inference (CI). VsCG module aims to alleviate the first correlation by integrating prior knowledge into prediction. Information of all the frames in current video is used to enhance the commonsense graph constructed from co-occurrence patterns of all training samples. Thus, the commonsense graph has been augmented with video-specific temporal dependencies. Then, a CI strategy with both intervention and counterfactual is used. The intervention component further eliminates the first correlation by forcing the model to consider all possible predicate categories fairly, while the counterfactual component resolves the second correlation by removing the bad effect from context. Comprehensive experiments on the Action Genome dataset show that the proposed method achieves state-of-the-art performance.