Video understanding has achieved great success in representation learning,
such as video caption, video object grounding, and video descriptive
question-answer. However, current methods still struggle on video reasoning,
including evidence reasoning and commonsense reasoning. To facilitate deeper
video understanding towards video reasoning, we present the task of
Causal-VidQA, which includes four types of questions ranging from scene
description (description) to evidence reasoning (explanation) and commonsense
reasoning (prediction and counterfactual). For commonsense reasoning, we set up
a two-step solution by answering the question and providing a proper reason.
Through extensive experiments on existing VideoQA methods, we find that the
state-of-the-art methods are strong in descriptions but weak in reasoning. We
hope that Causal-VidQA can guide the research of video understanding from
representation learning to deeper reasoning. The dataset and related resources
are available at https://github.com/bcmi/Causal-VidQA.git.

为了深化视频理解，包含描述、证据推理和常识推理等四类问题，我们提出了因果 - 视频问答任务及两步解决方案，发现最新 VideoQA 方法在描述方面表现强而在推理方面表现弱，希望 Causal-VidQA 能够引导视频理解研究从表示学习向深度推理发展。