Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering

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…

• NN
  National Natural Science Foundation of China

  Awards: 61976250, 62002395
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Awards: 2021A1515012311, 2023A1515011530