  Training multimodal networks requires a vast amount of data due to their
larger parameter space compared to unimodal networks. Active learning is a
widely used technique for reducing data annotation costs by selecting only
those samples that could contribute to improving model performance. However,
current active learning strategies are mostly designed for unimodal tasks, and
when applied to multimodal data, they often result in biased sample selection
from the dominant modality. This unfairness hinders balanced multimodal
learning, which is crucial for achieving optimal performance. To address this
issue, we propose three guidelines for designing a more balanced multimodal
active learning strategy. Following these guidelines, a novel approach is
proposed to achieve more fair data selection by modulating the gradient
embedding with the dominance degree among modalities. Our studies demonstrate
that the proposed method achieves more balanced multimodal learning by avoiding
greedy sample selection from the dominant modality. Our approach outperforms
existing active learning strategies on a variety of multimodal classification
tasks. Overall, our work highlights the importance of balancing sample
selection in multimodal active learning and provides a practical solution for
achieving more balanced active learning for multimodal classification.
