Measuring Compositional Consistency for Video Question Answering

Recent video question answering benchmarks indicate that state-of-the-art models struggle to answer compositional questions. However, it remains unclear which types of compositional reasoning cause models to mispredict. Furthermore, it is difficult to discern whether models arrive at answers using compositional reasoning or by leveraging data biases. In this paper, we develop a question decomposition engine that programmatically deconstructs a compositional question into a directed acyclic graph of sub-questions. The graph is designed such that each parent question is a composition of its children. We present AGQA-Decomp, a benchmark containing 2.3M question graphs, with an average of 11.49 sub-questions per graph, and 4.55M total new sub-questions. Using question graphs, we evaluate three state-of-the-art models with a suite of novel compositional consistency metrics. We find that models either cannot reason correctly through most compositions or are reliant on incorrect reasoning to reach answers, frequently contradicting themselves or achieving high accuracies when failing at intermediate reasoning steps. * Equal contribution Legend: objects relationships actions time Q. What is the first object that the person is touching after taking a picture? Q. Is a phone the first object that the person is touching after taking a picture? Q. Does a phone exist? Q. Is the person touching something? Q. Is the person taking a picture? Q. Does a person exist? Q. Is the person taking something? Q. Does a picture exist? Compositional question decomposition Q. What is the person touching after taking a picture? Q. Is a person touching something after taking a picture? Q. Does a person exist after taking a picture?