  Multi-modal methods establish comprehensive superiority over uni-modal
methods. However, the imbalanced contributions of different modalities to
task-dependent predictions constantly degrade the discriminative performance of
canonical multi-modal methods. Based on the contribution to task-dependent
predictions, modalities can be identified as predominant and auxiliary
modalities. Benchmark methods raise a tractable solution: augmenting the
auxiliary modality with a minor contribution during training. However, our
empirical explorations challenge the fundamental idea behind such behavior, and
we further conclude that benchmark approaches suffer from certain defects:
insufficient theoretical interpretability and limited exploration capability of
discriminative knowledge. To this end, we revisit multi-modal representation
learning from a causal perspective and build the Structural Causal Model.
Following the empirical explorations, we determine to capture the true
causality between the discriminative knowledge of predominant modality and
predictive label while considering the auxiliary modality. Thus, we introduce
the $\beta$-generalization front-door criterion. Furthermore, we propose a
novel network for sufficiently exploring multi-modal discriminative knowledge.
Rigorous theoretical analyses and various empirical evaluations are provided to
support the effectiveness of the innate mechanism behind our proposed method.
