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Interventional Imbalanced Multi-Modal Representation Learning via $β$-Generalization Front-Door Criterion

Yi Li, Fei Song, Changwen Zheng, Jiangmeng Li, Fuchun Sun, Hui Xiong

TL;DR

The paper tackles the problem of imbalanced modality contributions in multi-modal learning by introducing a causal view and a novel Interventional Imbalanced Multi-Modal Learning (IMML) framework. Central to IMML are two components: (1) a modality discriminative knowledge exploration network that learns and aligns per-modality discriminative features using a contrastive loss, and (2) a β-generalization front-door adjustment that estimates the causal effect of the predominant modality on the target while incorporating the auxiliary modality through a learned front-door mechanism. The authors derive the β-generalization front-door criterion, provide a dual-perspective derivation and a formal identifiability formula, and prove a generalization-bound showing that minimizing the discriminative knowledge loss improves out-of-distribution performance. Empirically, IMML yields significant improvements over strong baselines across multiple datasets and remains competitive with, or superior to, state-of-the-art plug-and-play methods, validating both its theoretical foundations and practical impact for robust, causally-informed multi-modal learning.

Abstract

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 $β$-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.

Interventional Imbalanced Multi-Modal Representation Learning via $β$-Generalization Front-Door Criterion

TL;DR

The paper tackles the problem of imbalanced modality contributions in multi-modal learning by introducing a causal view and a novel Interventional Imbalanced Multi-Modal Learning (IMML) framework. Central to IMML are two components: (1) a modality discriminative knowledge exploration network that learns and aligns per-modality discriminative features using a contrastive loss, and (2) a β-generalization front-door adjustment that estimates the causal effect of the predominant modality on the target while incorporating the auxiliary modality through a learned front-door mechanism. The authors derive the β-generalization front-door criterion, provide a dual-perspective derivation and a formal identifiability formula, and prove a generalization-bound showing that minimizing the discriminative knowledge loss improves out-of-distribution performance. Empirically, IMML yields significant improvements over strong baselines across multiple datasets and remains competitive with, or superior to, state-of-the-art plug-and-play methods, validating both its theoretical foundations and practical impact for robust, causally-informed multi-modal learning.

Abstract

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 -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.
Paper Structure (17 sections, 3 theorems, 29 equations, 7 figures, 5 tables, 1 algorithm)

This paper contains 17 sections, 3 theorems, 29 equations, 7 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Revisiting the Imbalanced MML from the Causal Perspective
  4. Structural Causal Model
  5. $\beta$-Generalization Front-Door Criterion
  6. Methodology
  7. Modality Discriminative Knowledge Exploration
  8. $\beta$-Generalization Front-Door Adjustment
  9. Theoretical Analysis
  10. Experiments
  11. Conclusion
  12. Preliminary of Causal Inference
  13. Derivation of $\beta$-Generalization Front-Door Adjustment
  14. Proof of Theorem \ref{['theorem:guarantee_for_generalization']}
  15. Datasets and Implementations
  16. ...and 2 more sections

Key Result

Theorem 3.2

(Front-Door Adjustment)If a set of variables $Z$ satisfy the front-door criterion relative to an ordered pair of variables $(X, Y)$, then the causal effect of $X$ on $Y$ is identifiable and is given by the following front-door adjustment formula pearl2009causal:

Figures (7)

  • Figure 1: (a): We provide an example in the MVSA-Single dataset mvsa. Concretely, we utilize the uni-modal logits in the state-of-the-art (SOTA) multi-modal method QMF DBLP:conf/icml/ZhangWZHFZP23 to get uni-modal predictions. The emotion in the text is predicted as positive, while that of the image is predicted as negative. (b): On the two multi-modal datasets (HFM hfm and MVSA-Single mvsa, in which text is predominant and image is auxiliary), our statistical results indicate that, in cases where the predicted labels from the predominant and auxiliary modalities are inconsistent, the ratio that label predicted by the predominant modality is identical with the ground truth label significantly exceeds that of the auxiliary modality. (c): When evaluating the performance of QMF, we freeze all parameters of QMF and mask specific dimensions of the latent multi-modal features randomly under different ratios. We plot the performance as the heatmap, in which the lighter the color, the greater the performance boosts. (d): We depict the experimental results of various MML methods. The results demonstrate that solely utilizing the predominant modality outperforms solely utilizing the auxiliary modality. QMF leverages both the predominant and auxiliary modalities to achieve further performance improvement. PMR fan2023pmr is an AMEM. QMF+PMR augments the auxiliary modality in QMF and outperforms the plain QMF, while QMF+Ours achieves superior performance compared to QMF+PMR.
  • Figure 2: The proposed SCM for the imbalanced MML. a) presents the plain SCM, and b) presents the determined $\alpha$ and $\beta$ back-door paths for the proposed SCM from the perspective of the front-door criterion.
  • Figure 3: We illustrate the framework of IMML with two modalities, i.e., text and image. F&T stands for the fusion module and target mapping module of any multi-modal model. Therefore, IMML can be treated as a plug-and-play component to boost the performance of MML within the imbalanced scenario.
  • Figure 4: The extended research of $\gamma_1$ and $\gamma_2$ on MVSA-Single, HFM, MVSA-Multiple and Food101.
  • Figure 5: The SCMs of three structures.
  • ...and 2 more figures

Theorems & Definitions (10)

  • Definition 3.1
  • Theorem 3.2
  • Definition 3.3
  • Definition 3.4
  • Definition 3.5
  • Theorem 3.6
  • Theorem 5.2
  • Definition 8.1
  • Definition 8.2
  • Definition 8.3