Knowledge Proxy Intervention for Deconfounded Video Question Answering


Recently, Video Question-Answering (VideoQA) has drawn more and more attention from both the industry and the research community. Despite all the success achieved by recent works, dataset bias always harmfully misleads current methods focusing on spurious correlations in training data. To analyze the effects of dataset bias, we frame the VideoQA pipeline into a causal graph, which shows the causalities among video, question, aligned feature between video and question, answer, and underlying confounder. Through the causal graph, we prove that the confounder and the backdoor path lead to spurious causality. To tackle the challenge that the confounder in VideoQA is unobserved and non-enumerable in general, we propose a model-agnostic framework called Knowledge Proxy Intervention (KPI), which introduces an extra knowledge proxy variable in the causal graph to cut the backdoor path and remove the effect of confounder. Our KPI framework exploits the front-door adjustment, which requires no prior knowledge about the confounder. The effectiveness of our KPI framework is corroborated by three baseline methods on five benchmark datasets, including MSVD-QA, MSRVTT-QA, TGIF-QA, NExT-QA, and Causal-VidQA.

Proceedings of the IEEE / CVF International Conference on Computer Vision (ICCV 2023)