标注关键样本：模态平衡与难度感知的多模态主动学习
Label What Matters: Modality-Balanced and Difficulty-Aware Multimodal Active Learning
将样本选择建模为马尔可夫决策过程，用强化学习策略动态调整模态权重与样本难度，可直接迁移到多模态视觉任务（如图像-文本、视频-音频）的主动学习标注流程中。
原文依据 we propose RL-MBA, a reinforcement-learning framework for modality-balanced, difficulty-aware multimodal active learning
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多模态学习整合来自图像、文本和音频等不同模态的互补信息以提升模型性能，但其成功依赖于大规模标注数据，而获取这些数据成本高昂。主动学习（AL）通过选择性标注信息量大的样本来缓解这一挑战。在多模态场景中，许多方法隐含地假设模态重要性在各轮次间保持稳定，并将选择规则固定在融合阶段，这使得它们对多模态学习的动态特性不敏感——随着训练推进，模态的相对价值和实例的难度都会发生变化。为解决这一问题，我们提出RL-MBA，一个用于模态平衡、难度感知的多模态主动学习的强化学习框架。RL-MBA将样本选择建模为马尔可夫决策过程，其中策略根据模态贡献、不确定性和多样性进行自适应调整，奖励则鼓励准确率提升和平衡。两个关键组件驱动了这种自适应性：（1）自适应模态贡献平衡（AMCB），通过强化反馈动态调整模态权重；（2）用于难度感知策略调整的证据融合（EFDA），通过基于不确定性的证据融合估计样本难度，以优先选择信息量大的样本。在Food101、KineticsSound和VGGSound上的实验表明，RL-MBA在有限标注预算下持续优于强基线，同时提升了分类准确率和模态公平性。
英文原文摘要 1550 字符
Multimodal learning integrates complementary information from different modalities such as image, text, and audio to improve model performance, but its success relies on large-scale labeled data, which is costly to obtain. Active learning (AL) mitigates this challenge by selectively annotating informative samples. In multimodal settings, many approaches implicitly assume that modality importance is stable across rounds and keep selection rules fixed at the fusion stage, which leaves them insensitive to the dynamic nature of multimodal learning, where the relative value of modalities and the difficulty of instances shift as training proceeds. To address this issue, we propose RL-MBA, a reinforcement-learning framework for modality-balanced, difficulty-aware multimodal active learning. RL-MBA models sample selection as a Markov Decision Process, where the policy adapts to modality contributions, uncertainty, and diversity, and the reward encourages accuracy gains and balance. Two key components drive this adaptability: (1) Adaptive Modality Contribution Balancing (AMCB), which dynamically adjusts modality weights via reinforcement feedback, and (2) Evidential Fusion for Difficulty-Aware Policy Adjustment (EFDA), which estimates sample difficulty via uncertainty-based evidential fusion to prioritize informative samples. Experiments on Food101, KineticsSound, and VGGSound demonstrate that RL-MBA consistently outperforms strong baselines, improving both classification accuracy and modality fairness under limited labeling budgets.