The label-preserving multimodal augmentation assumption in the CoMM framework posits that augmentations applied to multimodal data should not alter the task-relevant information contained within the data. While this assumption is beneficial for ensuring that the model learns meaningful representations, it has several potential limitations:
Overly Restrictive: The assumption may be too strict, as it limits the types of augmentations that can be applied. In practice, certain augmentations that do not preserve labels might still provide valuable information for learning, especially in complex tasks where noise and variability are inherent.
Task-Specificity: The assumption may not hold across different tasks or datasets. What preserves label information for one task may not be applicable to another, leading to a lack of generalizability in the augmentation strategies.
Complex Interactions: In multimodal scenarios, the interactions between modalities can be complex and may not always be captured by simple augmentations. The assumption may overlook the potential benefits of more sophisticated augmentations that could enhance the model's ability to learn unique and synergistic information.
To relax or improve this assumption in future work, researchers could consider the following approaches:
Adaptive Augmentation Strategies: Develop adaptive augmentation techniques that can dynamically adjust based on the specific characteristics of the data and the task at hand. This could involve using reinforcement learning to identify effective augmentations that balance label preservation with the introduction of variability.
Incorporating Noise: Introduce controlled noise into the data that does not directly alter the label but encourages the model to learn robust features. This could help the model generalize better to unseen data.
Exploratory Augmentations: Investigate augmentations that intentionally introduce variability or perturbations that may not strictly preserve labels but can still provide complementary information. This could involve using generative models to create realistic variations of the data.
By addressing these limitations, the CoMM framework can become more flexible and robust, allowing for a wider range of augmentations that enhance its learning capabilities.