Multimodal learning often grapples with the challenge of low-quality data, which predominantly manifests as two facets: modality imbalance and noisy corruption. While these issues are often studied in isolation, we argue that they share a common root in the predictive uncertainty towards the reliability of individual modalities and instances during learning. In this paper, we propose a unified framework, termed Conformal Predictive Self-Calibration (CPSC), which leverages conformal prediction to equip the model with the ability to perform self-guided calibration on-the-fly. The core of our proposed CPSC lies in a novel self-calibrating training loop that seamlessly integrates two key modules: (1) Representation Self-Calibration, which decomposes unimodal features into components, and selectively fuses the most robust ones identified by a conformal predictor to enhance feature resilience. (2) Gradient Self-Calibration, which recalibrates the gradient flow during backpropagation based on instance-wise reliability scores, steering the optimization towards more trustworthy directions. Furthermore, we also devise a self-update strategy for the conformal predictor to ensure the entire system co-evolves consistently throughout the training process. Extensive experiments on six benchmark datasets under both imbalanced and noisy settings demonstrate that our CPSC framework consistently outperforms existing state-of-the-art methods. Our code is available at https://github.com/XunCHN/CPSC.

翻译：多模态学习常面临低质量数据的挑战，主要表现为模态失衡与噪声污染两个层面。尽管这些问题通常被独立研究，但我们认为其共同根源在于学习过程中对单个模态及样本可靠性的预测不确定性。本文提出统一框架——共形预测自标定（Conformal Predictive Self-Calibration, CPSC），通过共形预测技术赋予模型实时自引导标定能力。CPSC的核心在于创新的自标定训练循环，其无缝整合两大关键模块：(1)表示自标定模块，将单模态特征分解为子成分，由共形预测器筛选出最稳健成分进行选择性融合以增强特征鲁棒性；(2)梯度自标定模块，基于样本级可靠性得分重新校准反向传播中的梯度流，引导优化向更可信方向演进。此外，我们为共形预测器设计了自更新策略，确保整个系统在训练过程中协同进化。在六个基准数据集上的大量实验表明，无论在失衡还是噪声场景下，CPSC框架均持续优于现有最优方法。代码开源地址：https://github.com/XunCHN/CPSC