← 研踪 PaperTrailCVF · CVPR 2026:cvf:2f02e46ed1833f1aff8b94a44499a470 · 记录 #1
Progressive Cross-Modal Causal Intervention for Long-Term Action Recognition
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-02 00:21
摘要
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 front-door 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.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
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 front-door 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.
查看此版本解读 →查看此版本原文 ↗