Learning Modality Knowledge Alignment for Cross-Modality Transfer
This work addresses the challenge of transferring knowledge across different data modalities for machine learning practitioners, representing an incremental advancement in cross-modality techniques.
The paper tackles the problem of cross-modality transfer by investigating how modality gaps hinder knowledge reuse, and it introduces MoNA, a meta-learning method that improves transfer performance over existing fine-tuning approaches.
Cross-modality transfer aims to leverage large pretrained models to complete tasks that may not belong to the modality of pretraining data. Existing works achieve certain success in extending classical finetuning to cross-modal scenarios, yet we still lack understanding about the influence of modality gap on the transfer. In this work, a series of experiments focusing on the source representation quality during transfer are conducted, revealing the connection between larger modality gap and lesser knowledge reuse which means ineffective transfer. We then formalize the gap as the knowledge misalignment between modalities using conditional distribution P(Y|X). Towards this problem, we present Modality kNowledge Alignment (MoNA), a meta-learning approach that learns target data transformation to reduce the modality knowledge discrepancy ahead of the transfer. Experiments show that out method enables better reuse of source modality knowledge in cross-modality transfer, which leads to improvements upon existing finetuning methods.