This paper proposes the first video-grounded entailment tree reasoning method for commonsense video question answering (VQA). Despite the remarkable progress of large visual-language models (VLMs), there are growing concerns that they learn spurious correlations between videos and likely answers, reinforced by their black-box nature and remaining benchmarking biases. Our method explicitly grounds VQA tasks to video fragments in four steps: entailment tree construction, video-language entailment verification, tree reasoning, and dynamic tree expansion. A vital benefit of the method is its generalizability to current video and image-based VLMs across reasoning types. To support fair evaluation, we devise a de-biasing procedure based on large-language models that rewrites VQA benchmark answer sets to enforce model reasoning. Systematic experiments on existing and de-biased benchmarks highlight the impact of our method components across benchmarks, VLMs, and reasoning types.

本研究解决了常识视频问答领域中视频与答案之间的虚假关联问题，提出了一种视频基础的推理树方法。该方法通过构建推理树、验证视频语言推理、树推理和动态树扩展四个步骤来实现，并且能够适应现有的视频和图像模型。实验结果表明，该方法在不同基准和推理类型中具有显著影响。

基于视频推理树的常识视频问答