Modality Capability Enhancement (MCE)

• Modality Capability Enhancement (MCE) is a framework that optimizes multi-modal learning by dynamically rebalancing training and feature quality across diverse sensors.
• It integrates Learning Capability Enhancement (LCE) and Representation Capability Enhancement (RCE) to diagnose and correct imbalances at both dataset and batch levels.
• Empirical benchmarks in applications like medical imaging and autonomous driving show that MCE significantly improves robustness under incomplete or degraded modality conditions.

Modality Capability Enhancement (MCE) refers to a set of strategies, architectures, and optimization objectives designed to improve both the learning dynamics and representational robustness of multi-modal machine learning systems, particularly in scenarios where modality incompleteness, imbalance, or degradation occurs. Modern MCE approaches seek to dynamically balance the training and feature quality across all available modalities, ensuring that each contributes effectively to downstream tasks—even when some modalities are missing frequently or exhibit poor signal quality. The framework detailed in (Zhao et al., 12 Oct 2025) proposes a general, modular solution to handle missing modalities under imbalanced missing rates, targeting improved multi-modal robustness in various domains such as medical imaging, autonomous driving, and affective computing.

1. Motivation and Core Challenges

In real-world multi-modal applications, the prevalence and utility of individual modalities—such as visual, auditory, text, or physiological signals—can vary significantly across data samples due to sensor failures, acquisition constraints, or subject-specific variability. This leads to imbalanced missing rates among modalities, creating a feedback loop where modalities that are absent more frequently receive fewer optimization updates, resulting in degraded representations and marginalization during training and inference. Traditional approaches that focus on global dataset-level balancing are insufficient because they fail to account for per-sample modality utility and the evolving quality of each modality’s features. MCE addresses this by dynamically diagnosing and treating representational and optimization imbalances at both global and local scales.

2. Learning Capability Enhancement (LCE)

LCE is designed to rebalance the optimization process so that all modalities—regardless of their observation frequency—achieve fair and sustained training. It operates through two key mechanisms:

• Dataset-Level Update Scaling (𝒜):

For each modality $m$, a global compensation factor $\mathcal{A}_m$ is computed as:

$\mathcal{A}_m = \frac{N}{\sum_n \mathcal{E}_{n,m}}$

where $N$ is the total number of samples and $\mathcal{E}_{n,m}$ is the presence indicator for modality $m$ in sample $n$. Normalization is applied to limit extreme scaling.

• Sample-Aware Batch-Level Enhancement (ℬ):

At the batch level, the framework employs a game-theoretic, Shapley value-based analysis to estimate each modality’s marginal contribution to instantaneous batch performance. For modality $m$ in a sample or batch, the Shapley value $\phi_m$ is given by:

$$\phi_m = \sum_{n} \sum_{S \subseteq \mathcal{M}_n \setminus \{m\}} \frac{|S|! (|\mathcal{M}_n| - |S| - 1)!}{|\mathcal{M}_n|!} [v_n(S \cup \{m\}) - v_n(S)]$$

where $v_n(S)$ reflects the task performance using subset $S$ of modalities for sample $n$. A modality-specific performance upperbound $\mathcal{U}_m$ is defined using a reference single-modal model. The capability gap $\Delta_m = \mathcal{U}_m - \phi_m$ quantifies the needed correction and is compounded with the occurrence count during the batch to yield $\mathcal{B}_m$. ℬ boosts the learning rate for underperforming, underrepresented modalities on a per-batch basis.

3. Representation Capability Enhancement (RCE)

While LCE governs training dynamics, RCE enforces semantic quality and cross-modal recoverability of representations:

• Single-Modal Supervision:

Each modality-specific encoder is trained with a supervised loss (𝓛_single), weighted by current values of 𝒜 and ℬ, providing stronger supervision to modalities that are under-optimized or degraded.

• Subset Prediction (Subset-Task Supervision):

For any non-empty subset $S$ of the available modalities in a training batch, the model is required to perform the main task relying only on features from $S$. The subset-task loss (𝓛_sub) ensures that representations from any subset are maximally complementary and robust.

• Cross-Modal Completion (Auxiliary Completion Supervision):

The model is also trained to reconstruct missing modality features from available ones using a dedicated reconstruction module (e.g., Transformer). The auxiliary completion loss (𝓛_aux), again modulated by 𝒜 and ℬ, reinforces a joint latent space that supports reliable cross-modal inference and alignment.

• Combined Loss:

The total objective is:

$$\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{task}} + \lambda_{\text{single}} \mathcal{L}_{\text{single}} + \lambda_{\text{sub}} \mathcal{L}_{\text{sub}} + \lambda_{\text{aux}} \mathcal{L}_{\text{aux}}$$

with task-specific, single-modal, subset, and completion losses, and tunable $\lambda$ coefficients.

4. Diagnosis–Treatment Loop for Imbalanced Missing Rates

MCE executes an online, dual-phase diagnosis–treatment process:

• At each optimization step, global (𝒜) and local (ℬ) signals are updated to reflect both the long-term missing rate statistics and immediate modality utility.
• Training targets modalities with high capability gaps and low observation frequencies, effectively closing the optimization and representational imbalance.
• The subset prediction loss (𝓛_sub) ensures that even rare modality combinations are explicitly supported, while the auxiliary completion loss (𝓛_aux) preserves cross-modal consistency.

This feedback loop systematically mitigates the “vicious cycle” whereby missing modalities become progressively weaker and guarantees that all modalities make substantial and stable contributions to the fused representation.

5. Empirical Performance and Benchmarks

Extensive experiments confirm the efficacy of MCE across standard benchmarks with wide-ranging incomplete and imbalanced modality conditions, including:

• Urban scene segmentation (nuScenes), brain tumor segmentation (BraTS2020), multi-speaker emotion recognition (IEMOCAP), and audio-visual digit recognition (AudiovisionMNIST).
• Performance gains are reported in task-relevant metrics such as mIoU, Dice score, and accuracy for subsets and full modality configurations.
• Ablation studies demonstrate that both LCE and RCE components are essential: removing either significantly impairs robustness and performance under severe modality absence.
• Notably, the game-theoretic Shapley value calculus enables precise, local, and dynamic diagnosis of modality utility, often leading to substantial improvement for underrepresented modalities.

6. Mathematical Underpinnings and Optimization

Key equations distilled from the approach include:

• Global update factor:

$\mathcal{A}_m = N / (\sum_n \mathcal{E}_{n,m})$

• Shapley value for batch modality contribution:

$$\phi_m = \sum_n \sum_{S \subseteq \mathcal{M}_n \setminus \{m\}} \frac{|S|! \ (|\mathcal{M}_n| - |S| - 1)!}{|\mathcal{M}_n|!} [ v_n(S \cup \{m\}) - v_n(S) ]$$

• Capability gap and incentive calculation:

$\Delta_m = \mathcal{U}_m - \phi_m$, $\mathcal{B}_m$ derived from batch-wise analysis (details see (Zhao et al., 12 Oct 2025), Sec. 4.2).

• Auxiliary reconstruction and subset-task losses:

Structured for all subset combinations of modalities (exponential in number for $|\mathcal{M}_n|$).

7. Practical Implications and Deployment

The MCE framework is highly relevant in domains where modality presence is inconsistent due to environmental, technical, or operational constraints. It is directly applicable to:

• Multi-sensor fusion in autonomous systems, especially when sensors intermittently fail or yield unusable data.
• Medical imaging scenarios with partial or missing scans, facilitating robust diagnostic inference.
• Multi-party social signal processing where sensor availability and data quality fluctuate.
• Multimodal conversational AI with sporadically missing audio, video, or text streams.

Crucially, MCE delivers reliable modality fusion and robust predictions even when missing rates are highly imbalanced—a regime where previous state-of-the-art methods fail to guarantee stable feature utility or task performance.

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In summary, Modality Capability Enhancement, as instantiated in (Zhao et al., 12 Oct 2025), formalizes a general approach for addressing the under-optimization and representational degradation of modalities under imbalanced missingness. By integrating multi-level dynamic reweighting (LCE) and multi-task representation controls (RCE), MCE advances robust multi-modal learning, achieving superior and resilient performance across diverse pattern recognition tasks and deployment scenarios faced with incomplete modality data.