Cross-Modal Dual-Causal Learning for Long-Term Action Recognition

TL;DR

This paper introduces CMDCL, a novel cross-modal dual-causal learning framework that improves long-term action recognition by modeling causal relationships between videos and texts, addressing biases and confounders.

Contribution

It proposes a structural causal model for cross-modal causal learning in LTAR, incorporating dual causal interventions to enhance robustness over existing methods.

Findings

Outperforms baselines on Charades, Breakfast, and COIN datasets.

Effectively removes cross-modal biases and visual confounders.

Demonstrates robustness in long-term action recognition tasks.

Abstract

Long-term action recognition (LTAR) is challenging due to extended temporal spans with complex atomic action correlations and visual confounders. Although vision-language models (VLMs) have shown promise, they often rely on statistical correlations instead of causal mechanisms. Moreover, existing causality-based methods address modal-specific biases but lack cross-modal causal modeling, limiting their utility in VLM-based LTAR. This paper proposes \textbf{C}ross-\textbf{M}odal \textbf{D}ual-\textbf{C}ausal \textbf{L}earning (CMDCL), which introduces a structural causal model to uncover causal relationships between videos and label texts. CMDCL addresses cross-modal biases in text embeddings via textual causal intervention and removes confounders inherent in the visual modality through visual causal intervention guided by the debiased text. These dual-causal interventions enable robust action representations to address LTAR challenges. Experimental results on three benchmarks including Charades, Breakfast and COIN, demonstrate the effectiveness of the proposed model. Our code is available at https://github.com/xushaowu/CMDCL.