Progressive Cross-Modal Causal Intervention for Long-Term Action Recognition

Intricate correlations among atomic actions and inherent visual confounders in long-term action recognition (LTAR) contribute to the persistent challenges in this domain. While methods based on vision-language models that employ label text for supervision offer potential for handling visual confounders, their reliance on statistical correlations rather than causal mechanisms introduces two vulnerabilities: (1) spurious alignments with non-causal co-occurring visual features during cross-modal interaction, and (2) misinterpretation of codependencies among actions. To address these limitations, this paper introduces Progressive Cross-Modal Causal Intervention (PCMCI). PCMCI first mitigates co-occurrence hallucination via causal intervention grounded in optimal transport theory. Subsequently, an action relation-aware mechanism counters the backdoor path induced by codependency illusion, enabling the derivation of deconfounded text embeddings. Finally, these deconfounded embeddings serve as mediator to implement frontdoor adjustment to remove visual confounders. This progressive causal intervention framework facilitates learning robust representations for LTAR. Experiments on three long-term action benchmarks demonstrate the effectiveness of the proposed model.