标题： RCMCL：一种统一的对比学习框架，用于鲁棒的多模态（RGB-D、骨骼、点云）动作理解 显示英文标题

标题： RCMCL: A Unified Contrastive Learning Framework for Robust Multi-Modal (RGB-D, Skeleton, Point Cloud) Action Understanding

摘要： 人体动作识别（HAR）使用多模态输入（RGB-D、骨骼、点云）可以实现高精度，但通常依赖于大型标注数据集，并且在传感器故障或噪声情况下性能会急剧下降。 我们提出了鲁棒的跨模态对比学习（RCMCL），这是一种自监督框架，能够学习模态不变表示，并在模态丢失和损坏的情况下保持可靠性。 RCMCL联合优化（i）一个跨模态对比目标，对齐异构流，（ii）一个模态内自我蒸馏目标，提高视图不变性并减少冗余，以及（iii）一个退化模拟目标，明确训练模型从被遮挡或损坏的输入中恢复。 在推理时，自适应模态门控（AMG）网络为每个模态分配数据驱动的可靠性权重以进行鲁棒融合。 在NTU RGB+D 120（CS/CV）和UWA3D-II上，RCMCL在标准设置中达到了最先进的准确率，并表现出显著更好的鲁棒性：在严重的双模态丢失情况下，仅表现出11.5%的下降，明显优于强大的监督融合基线。 这些结果表明，自监督的跨模态对齐，结合显式的退化建模和自适应融合，是可部署的多模态HAR的关键。 显示英文摘要

摘要： Human action recognition (HAR) with multi-modal inputs (RGB-D, skeleton, point cloud) can achieve high accuracy but typically relies on large labeled datasets and degrades sharply when sensors fail or are noisy. We present Robust Cross-Modal Contrastive Learning (RCMCL), a self-supervised framework that learns modality-invariant representations and remains reliable under modality dropout and corruption. RCMCL jointly optimizes (i) a cross-modal contrastive objective that aligns heterogeneous streams, (ii) an intra-modal self-distillation objective that improves view-invariance and reduces redundancy, and (iii) a degradation simulation objective that explicitly trains models to recover from masked or corrupted inputs. At inference, an Adaptive Modality Gating (AMG) network assigns data-driven reliability weights to each modality for robust fusion. On NTU RGB+D 120 (CS/CV) and UWA3D-II, RCMCL attains state-of-the-art accuracy in standard settings and exhibits markedly better robustness: under severe dual-modality dropout it shows only an 11.5% degradation, significantly outperforming strong supervised fusion baselines. These results indicate that self-supervised cross-modal alignment, coupled with explicit degradation modeling and adaptive fusion, is key to deployable multi-modal HAR.