Existing visual question answering methods often suffer from cross-modal spurious correlations and oversimplified event-level reasoning processes that fail to capture event temporality, causality, and dynamics spanning over the video. In this work, to address the task of event-level visual question answering, we propose a framework for cross-modal causal relational reasoning. In particular, a set of causal intervention operations is introduced to discover the underlying causal structures across visual and linguistic modalities. Our framework, named Cross-Modal Causal RelatIonal Reasoning (CMCIR), involves three modules: i) Causality-aware Visual-Linguistic Reasoning (CVLR) module for collaboratively disentangling the visual and linguistic spurious correlations via front-door and back-door causal interventions; ii) Spatial-Temporal Transformer (STT) module for capturing the fine-grained interactions between visual and linguistic semantics; iii) Visual-Linguistic Feature Fusion (VLFF) module for learning the global semantic-aware visual-linguistic representations adaptively. Extensive experiments on four event-level datasets demonstrate the superiority of our CMCIR in discovering visual-linguistic causal structures and achieving robust event-level visual question answering. The datasets, code, and models are available at https://github.com/HCPLab-SYSU/CMCIR.

翻译：现有的视觉问答方法常受跨模态虚假相关性影响，且对事件级推理过程过于简化，未能充分捕捉视频中的事件时序性、因果关系及动态变化。针对事件级视觉问答任务，本文提出一种跨模态因果关系推理框架。具体而言，我们引入一组因果干预操作，以发现视觉与语言模态间的潜在因果结构。所提出的框架名为跨模态因果关系推理（CMCIR），包含三个模块：i）因果感知视觉-语言推理（CVLR）模块，通过前门和后门因果干预协作解耦视觉与语言的虚假相关性；ii）时空Transformer（STT）模块，捕捉视觉与语言语义间的细粒度交互；iii）视觉-语言特征融合（VLFF）模块，自适应学习全局语义感知的视觉-语言表示。在四个事件级数据集上的大量实验表明，我们的CMCIR在发现视觉-语言因果结构及实现稳健事件级视觉问答方面具有优越性。数据集、代码及模型已开源至https://github.com/HCPLab-SYSU/CMCIR。