Understanding Multimodal Learning from Modality Fusion and Alignment Perspectives

Despite significant advancements in multimodal learning (MML), it has been unexpectedly shown to underperform compared to unimodal approaches in practice, largely due to the modality imbalance problem, ultimately affecting the overall performance of the model. Naturally, most existing methods aim to rebalance optimization speeds across different modalities to avoid performance degeneration caused by modality imbalance. However, in addition to task-oriented modality fusion, we experimentally find that multimodal learning requires explicit modality alignment to stimulate weak modal capabilities so that they can be fully exploited, which is ignored by existing works. Therefore, in this paper, we explore the impact of modality fusion and alignment on multimodal learning from a unified perspective, and develops a dynamic strategy that jointly optimizes both, with particular emphasis on addressing modality imbalance. Concretely, we initially design a soft alignment strategy to impose the positive intervention from the prediction level by integrating modality fusion and alignment into a unified framework. We further extend this strategy to the representation level and hybrid level, enabling compatibility with a wider range of architectures. Subsequently, we design a heuristic strategy to dynamically integrate fusion and alignment. Furthermore, we develop a learning-based strategy using a bi-level optimization framework and theoretically prove the convergence of the learning algorithm to ensure its reliability. These two dynamic integration strategies are incorporated into a unified framework applicable to both supervised and semi-supervised scenarios, further enhancing performance. We conduct a series of experiments to demonstrate the effectiveness of our method on diverse datasets. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art multimodal learning approaches, achieving accuracy improvements of 1.30%, 2.69%, and 0.60% on representative bimodal benchmarks, namely KSounds, CREMA-D, andSarcasm, respectively, as well as gains of 1.35% and 0.65% on trimodal datasets, namely NVGesture and IEMOCAP.