CaST-Bench：面向视频问答的因果链时空推理基准
CaST-Bench: Benchmarking Causal Chain-Grounded Spatio-Temporal Reasoning for Video Question Answering
AI总结：
提出CaST-Bench基准，通过构建包含因果链时空标注的数据集和评估指标，系统评测视觉语言模型在视频因果推理中的细粒度证据定位能力。
摘要：
视频中的因果推理对视觉语言模型（VLM）是一个重大挑战，因为它需要超越表面感知，深入理解因果机制。然而，现有基准很少提供严格评估这一能力所需的细粒度、有依据的证据。为填补这一空白，我们引入了CaST-Bench，一个用于因果链时空视频推理的基准。CaST-Bench提出复杂的因果问题，要求模型识别并定位多个时空证据组成的链条。通过人机协作流程，我们构建了一个高质量数据集，包含1015个视频上的2066个问题，因果链由时间片段和边界框轨迹标注。此外，我们设计了一套全面的评估方案，包含新颖的指标，不仅评估答案正确性，还评估基于视觉证据的推理能力。这种证据基础对于通过减轻虚假相关性来提高准确性，以及通过使模型更透明来增强用户信任至关重要。我们的实验表明，当前的VLM在因果问题上表现不佳，主要原因是它们构建精确且有依据的因果链的能力有限。这为改进未来VLM指明了一个重要方向。
英文摘要：
Cause-and-effect reasoning in video is a significant challenge for Vision-Language Models (VLMs), as it requires going beyond surface-level perception to a deeper understanding of causal mechanisms. However, existing benchmarks rarely provide the fine-grained, grounded evidence needed to rigorously evaluate this capability. To address this gap, we introduce CaST-Bench, a benchmark for Causal Chain-Grounded Spatio-Temporal Video Reasoning. CaST-Bench presents complex causal questions that require models to identify and localize a chain of multiple spatio-temporal evidences. Through a human-AI collaborative pipeline, we construct a high-quality dataset of 2,066 questions over 1,015 videos, with causal chains annotated by temporal segments and bounding-box tracks. Furthermore, we design a comprehensive evaluation suite with novel metrics that assess not only answer correctness but also the capability for visual evidence grounded reasoning. This grounding is crucial for improving accuracy by mitigating spurious correlations and for enhancing user trust by making models more transparent. Our experiments show that current VLMs struggle with causal questions, largely due to their limited ability to construct precise and grounded causal chains. This highlights an important direction for improving future VLMs. Homepage: https://woven-by-toyota.github.io/CaST-Bench.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
