# From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering Source: [From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering]() Collection index: [llms.txt]() Data updated: 2025-12-24T17:21:18.924Z Authors: Jiangtong Li, Li Niu, Liqing Zhang Published: 2022-05-30T07:26:54.000Z ## Abstract 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 \\url{https://github.com/bcmi/Causal-VidQA.git}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()