Modality-Consistent Representation Learning

• MCRL is a suite of techniques that synchronizes feature representations across diverse modalities by minimizing distribution gaps at both class and instance levels.
• It employs methods such as class-wise alignment losses, adversarial training, and fine-grained token matching to ensure semantic consistency across inputs.
• Enhanced by generative fusion modules like Brownian-Bridge diffusion and attention-based mechanisms, MCRL significantly improves cross-modal retrieval and synthesis.

Modality-Consistent Representation Learning (MCRL) is a set of techniques and objectives designed to explicitly align feature representations across differing input modalities, thereby mitigating modality gaps in cross-modal tasks such as retrieval, generation, or classification. MCRL addresses the problem that different sensing modalities—such as optical and SAR imagery, vision and text, or RGB and depth—tend to induce features in disparate subspaces, which undermines generalization and cross-modal reasoning. Recent research formalizes MCRL with both distribution-level alignment losses and inference-time data generation/fusion, resulting in substantial improvements in cross-modal performance in application scenarios including ship re-identification and medical report generation (Zhao et al., 3 Dec 2025, Han et al., 10 Dec 2024).

1. Concept and Motivation

MCRL techniques are motivated by the empirical observation that simply co-training on multiple modalities often leaves significant modality gaps, evidenced by low cross-modal retrieval accuracy (e.g., optical queries retrieve only optical samples). These discrepancies persist even with shared backbone encoders, as inter-modal statistics and content differ markedly. The goal of MCRL is to force the feature distributions for the same semantic instance, observed in different modalities, to coincide at the class level or instance level, thereby enabling robust semantic matching and transfer across modalities (Zhao et al., 3 Dec 2025).

The paradigm is especially critical in high-gain scenarios:

• Cross-domain ship re-identification (Optical–SAR), where inter-modal variation can eclipse intra-class variation (Zhao et al., 3 Dec 2025).
• Cross-modal retrieval with adversarially-learned shared spaces for, e.g., text-to-video or vision-LLMs (Liu et al., 2022, Liu et al., 2023).
• 2D-3D industrial anomaly detection, where geometric and appearance cues are poorly aligned but both inform prediction (Ali et al., 20 Oct 2025).

2. Representative Methodologies for Modality Alignment

Several techniques instantiate MCRL, often in combination:

• Class-wise modality alignment loss (CMAL): Minimizes the squared Euclidean distance between class means and variances for each modality in the embedding space. For identity c, with μ_opt^c and μ_sar^c as mean optical/SAR features, and var_opt^c, var_sar^c as their variances, the loss is

$$L_\text{cmal} = \frac{1}{|C|} \sum_c \left( \|\mu_\text{opt}^c - \mu_\text{sar}^c\|^2 + \|\text{var}_\text{opt}^c - \text{var}_\text{sar}^c\|^2 \right)$$

This objective is used in MOS for ship re-identification (Zhao et al., 3 Dec 2025).

• Adversarial learning for semantic mapping: Feature mapping networks (generators) project per-modality features into a shared semantic space, while a discriminator adversarially encourages indistinguishability between modalities. The semantic-consistency and inter-modal deviation losses ensure information preservation and alignment (Liu et al., 2022).
• Fine-grained alignment losses: Mean-squared error between corresponding tokens in video and text modalities strengthens token-wise correspondences in video-LLMs such as Video-Teller (Liu et al., 2023).
• Latent code reconstruction from fused representation: Multi-modal fusion encoders are optimized such that latent codes can reconstruct the input in each modality, enforcing information conservation and alignment (Ali et al., 20 Oct 2025).

3. Integration with Cross-Modal Data Generation and Feature Fusion

Alignment in the latent space is often insufficient at inference, especially for out-of-domain or noisy queries. Enhanced MCRL frameworks synthesize missing modalities during inference and fuse features to achieve better cross-modal consistency. Notably:

• Brownian-Bridge Diffusion Models (BBDM): These interpolate between cross-modal feature vectors to synthesize plausible representations in the target modality, which are then fused (weighted average with a fusion parameter τ) with the original features. This mechanism is central to the Cross-modal Data Generation and Feature Fusion (CDGF) module in MOS, yielding large boosts in cross-modal retrieval accuracy (Zhao et al., 3 Dec 2025).
• Attention-based cross-modal fusion: Features extracted from each modality are self- and cross-attended, then fused using learned gates or attention heads. For example, MedCLIP-based report generation fuses image embeddings and retrieved report embeddings with scalar/vector gating (Han et al., 10 Dec 2024).
• Adversarial cross-modal data generation: Residue-Fusion GAN leverages adversarial losses, feature-matching, and perceptual losses to create modality-consistent visual-tactile data, strengthening the cross-alignment (Cai et al., 2021).

4. Mathematical Formalization and Training Procedures

Typical MCRL objectives are combined with downstream task losses (e.g., classification, retrieval, or generation). Training procedures often feature batch-level computation of both intra-modality and inter-modality statistics for CMAL, adversarial generator/discriminator updates, and, where relevant, generative loss terms for synthesized cross-modal samples. In the MOS framework:

• The overall loss is given by a weighted combination of identity classification loss, triplet (metric) loss, and CMAL:

$$L_\text{total} = \lambda_\text{id} L_\text{id} + \lambda_\text{tri} L_\text{tri} + \lambda_\text{cmal} L_\text{cmal}$$

This is jointly trained with the Brownian-Bridge diffusion generator, which itself is supervised by:

$$\mathcal{L}_\text{diff} = \mathbb{E}_{x_0, y, t, \epsilon} \| \epsilon - \epsilon_\theta(x_t, t, y) \|^2$$

where $$x_t = (1-m_t)x_0 + m_t y + \sqrt{\delta_t}\,\epsilon$$ (Zhao et al., 3 Dec 2025).

• In adversarial frameworks, the generator and modality discriminator alternate updates to explicitly minimize modality cues in the shared space, while semantic-consistency losses preserve cross-instance information (Liu et al., 2022).

5. Empirical Evaluation and Significance

MCRL-based approaches demonstrate significant improvements over baselines in diverse cross-modal benchmarks:

Setting/Protocol	Baseline R1	+MCRL	+MCRL+CDGF (MOS full)
ALL2ALL (HOSS)	65.9%	68.2%	68.8%
Optical→SAR (O2S)	33.8%	38.5%	40.0%
SAR→Optical (S2O)	29.9%	40.3%	46.3%

The largest gains occur in strict cross-modal protocols, validating the ability of MCRL/CDGF strategies to close challenging modality gaps (Zhao et al., 3 Dec 2025). Similar gains are observed in medical report generation and video-language captioning tasks, confirming the generality of the principle (Han et al., 10 Dec 2024, Liu et al., 2023).

Ablation studies indicate that distribution alignment terms have consistent effect, but are most effective when paired with generative/fusion modules that structurally fill modality gaps during inference. Notably, fusion hyperparameter τ in MOS is robust with optimal values near τ ≈ 0.2.

6. Generalization to Other Application Domains

The MCRL and CDGF design concepts generalize beyond specific modalities:

• In medical vision-language tasks, retrieval of related text (reports) for given images, fusion via gated attention, and decoding with Transformer achieves state-of-the-art metrics on radiology report generation (Han et al., 10 Dec 2024).
• In video-language modeling, cascaded Q-Former modules align frame and ASR information at fine granularity, with explicit token-wise alignment loss reducing hallucinations and boosting summarization accuracy (Liu et al., 2023).
• In industrial defect detection, multi-modal fusion encoders and attention-guided decoders synthesize and restore both visual and geometric features, with cross-modal consistency enforced by joint loss (Ali et al., 20 Oct 2025).
• The approach is applicable to cross-domain object detection, speech-text retrieval, and multimodal dialogue, where aligning representations enables fusion of cues and joint reasoning.

7. Challenges and Open Research Directions

While MCRL has demonstrated strong empirical success, several challenges remain:

• Retrieval and generative noise: Synthesis and retrieval-based approaches risk the introduction of noisy cross-modal samples, which can slightly degrade pure single-modality performance if not properly controlled (Han et al., 10 Dec 2024).
• Scalability: Computing class-wise alignment in large-label or open-set regimes presents speed and memory bottlenecks.
• Modality missingness: Robustness when some modalities are absent at inference is an area of ongoing development.
• Tuning alignment–task loss balance: Optimal weighting of alignment, generative, and downstream task losses remains dataset- and protocol-dependent.

Continued research focuses on adaptive fusion, self-supervised alignment, and more generalized forms of invariant feature synthesis applicable to highly diverse or underconstrained modality pairs.

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References

• (Zhao et al., 3 Dec 2025): "MOS: Mitigating Optical-SAR Modality Gap for Cross-Modal Ship Re-Identification"
• (Han et al., 10 Dec 2024): "Integrating MedCLIP and Cross-Modal Fusion for Automatic Radiology Report Generation"
• (Liu et al., 2023): "Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling"
• (Ali et al., 20 Oct 2025): "2D_3D Feature Fusion via Cross-Modal Latent Synthesis and Attention Guided Restoration for Industrial Anomaly Detection"
• (Liu et al., 2022): "Cross-modal Search Method of Technology Video based on Adversarial Learning and Feature Fusion"
• (Cai et al., 2021): "Visual-Tactile Cross-Modal Data Generation using Residue-Fusion GAN with Feature-Matching and Perceptual Losses"