Multimodal Semantic Extraction

• Multimodal semantic extraction is the process of deriving aligned, structured semantic representations from heterogeneous data sources like text, images, audio, and video.
• Recent approaches employ retrieval-based reformulations and cross-modal alignment techniques to overcome modality gaps and achieve robust, zero-shot semantic retrieval.
• Advanced methods integrate lightweight fusion, variational information bottlenecks, and graph-based regularization to boost scalability and mitigate noise across diverse modalities.

Multimodal semantic extraction refers to the process of deriving and aligning structured semantic representations from heterogeneous data sources, including text, images, audio, video, and structured documents. The principal goal is to fuse, disentangle, and retrieve fine-grained semantics across modalities, enabling both robust information extraction (e.g., entity/relation typing, segmentation, reasoning) and increased interpretability for downstream tasks such as retrieval-augmented QA, semantic communications, or generative modeling. This process must address key challenges relating to modality gaps, semantic ambiguity, noisy signals, and scalability, and its solutions span retrieval-based paradigms, information-theoretic regularization, cross-modal fusion architectures, graph-based alignment, and advanced codebook tokenization.

1. Retrieval-Based Reformulations and Semantic Label Expansion

Recent advances, such as the ROC framework (Hei et al., 25 Sep 2025), advocate a paradigm shift from classical softmax-based classification of multimodal relations to retrieval-based reformulations. Here, each relation label is represented by a free-text natural-language description, which is mapped into a semantic embedding space (typically via a BERT or LLM encoder). For each entity pair detected in text/image, the model retrieves the best-matching relation description via a cosine similarity search, and contrastive semantic alignment loss is optimized to reflect this retrieval process: $$L = -\frac{1}{N} \sum_{i=1}^N \log \frac{\exp(\mathrm{sim}(\mathbf{e}_i, \mathbf{r}_i^+)/\tau)}{\sum_{j=1}^N \exp(\mathrm{sim}(\mathbf{e}_i, \mathbf{r}_j)/\tau)}$$ This approach facilitates richer semantic expressiveness, interpretability (model outputs human-readable relation descriptions), and flexible zero-shot extensibility (new relations are added as new descriptions rather than retraining label indices). Structural constraints (entity type, position) are explicitly encoded to prune the candidate label space, boosting recall and precision on non-trivial relation types.

2. Multimodal Alignment and Fusion Strategies

Addressing the semantic and modality gap across heterogeneous inputs is central to multimodal extraction. One direction is explicit alignment of all modalities into a unified semantic space, as in LAM-MSC (Jiang et al., 2023), where the MMA (Multimodal Alignment) module uses large multimodal LLMs to project all signals (text, image, audio, video) into latent text descriptions, enforced by semantic consistency and pairwise alignment losses. These latent representations are conditioned for further extraction or recovery, including prompt-personalized summaries via LLMs (LKB). Another approach, Shap-CA (Luo et al., 2024), introduces an Image–Context–Text interaction paradigm, relying on LMM-generated descriptive textual contexts to bridge semantic/modal gaps; a Shapley-value–based contrastive alignment quantifies and utilizes the playerwise contributions (context, image, text) toward representation overlap, followed by gated cross-attention fusion.

Lightweight fusion architectures, exemplified by LMFNet (Wang et al., 2024), utilize weight-sharing multi-branch transformers and staged feature fusion (feature reconstruction layers, multimodal self-attention) to integrate multi-spectral inputs (RGB, NIR, DSM) for high-resolution remote sensing segmentation at low parameter cost. In communication systems, prompt-based pre-training and cross-modality prompting (ProMSC-MIS (Zhang et al., 25 Aug 2025)) enhance the diversity and complementarity of unimodal semantic encoders prior to fusion—crucial for robust multi-spectral segmentation and semantic channel coding.

3. Information-Theoretic Regularization and Mixture-of-Experts

Robust semantic extraction demands suppressing modality-irrelevant noise while retaining maximal predictive information. MMIB (Cui et al., 2023) and MG-VMoE (Zhou et al., 21 Feb 2025) employ variational information bottleneck regularizers that enforce, for each modality-specific latent, minimal mutual information with the input (compressing noise) but maximal mutual information with the output label.

$\mathcal{L}_{IB} = I(Z;X) - \beta I(Z;Y)$

For fine-grained alignment, MG-VMoE routes (tokenized) joint text-image sequences through a sparse mixture-of-experts backbone where each expert is a VIB. Token-level gates ensure that experts learn complementary patterns, and semantic prototypes (BERT-encoded label representations) guide inference for zero-shot classification. A batchwise graph-based VAT regularizer encourages local smoothness under adversarial perturbations in the fused sample space.

4. Interpretability, Feature Disentanglement, and Modality Auditing

A line of research aims to reverse-engineer and audit the inner workings of multimodal embedding models by decomposing their feature spaces. Yan et al.'s Multi-Faceted Multimodal Monosemanticity (Yan et al., 16 Feb 2025) introduces sparse autoencoders and non-negative contrastive heads to disentangle CLIP’s polysemantic neurons into interpretable monosemantic features. Each dimension is scored for modality dominance (vision, language, or cross-modal) via activation ratios over held-out image–text pairs. Evaluative protocols confirm that these intrinsic decompositions align with human intuition and enable practical interventions—such as bias mitigation in gender detection, modality-targeted adversarial defenses, and controlled semantic transfer in generative models. The enrichment of modality-specific interpretability empowers transparent and safe multimodal AI deployment.

5. Extraction from Structured and Low-Resource Modalities

Semantic extraction from structured or low-resource multimodal sources involves specialized reasoning. TalentMine (Mannam et al., 22 Jun 2025) tackles tabular extraction via multimodal encoding (layout, visual, and textual features of table cells) combined with domain-aware LLM prompts to generate semantically enriched, structured text. This preserves both the logical and spatial dimension, enabling retrieval-augmented QA workflows that outperform traditional OCR/visual QA baselines (100% vs. ≤40% accuracy in sampled HR tasks). Likewise, MFCN (Yang et al., 2017) reformulates document semantic extraction as pixel-wise segmentation, fusing visual and OCR-derived textual features within a fully convolutional network, leveraging synthetic document generation for robust pretraining.

For indigenous, ideographic scripts with high semantic density and low annotated data, as in DongbaMIE (Bi et al., 5 Mar 2025), multimodal semantic extraction reveals the limitations of state-of-the-art LLMs and LMMs, which fail to jointly reason about object, action, relation, and attribute even under supervised fine-tuning (F1 < 12% for sentence-level object/action; ~0% for relation/attribute). The architecture and evaluation framework motivate research into hierarchical pretraining, instruction-tuning, and advanced token-level grounding.

6. Semi-Supervised and Sample-Efficient Alignment Methods

Space-structure correlation (Zheng et al., 2018) offers a semi-supervised route for coreference and semantic matching across modalities. By constructing high-level feature spaces per modality and representing each object as its Euclidean distance to a shared anchor set, one aligns modalities via a simple linear transformation and regularizes on both labeled and unlabeled pairs. With as few as a handful of labeled anchors, competitive retrieval accuracy is achieved compared to traditional CCA-based methods. The sample efficiency and generality render this strategy attractive for low-resource and highly heterogeneous datasets.

7. Scalability, Web-Scale Extraction, and Multilingual Generalization

High-throughput multimodal semantic extraction at web scale is feasible via jointly-trained, multi-task Transformer architectures (Cai et al., 2021). By embedding all token and categorical inputs with shared modality, document-type, and language embeddings, training proceeds over pooled multi-type, multi-language data with concurrent task heads (span, link, type, cluster). This yields scalable extraction and linking systems with strong cross-lingual transfer, efficient capacity sharing (∼7× inference speedup), and improved label consistency via majority aggregation. Scalability, noisiness reduction, and multi-tenant serving enable practical deployment for billions of documents daily.

• "Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction" (Hei et al., 25 Sep 2025)
• "Large AI Model Empowered Multimodal Semantic Communications" (Jiang et al., 2023)
• "TalentMine: LLM-Based Extraction and Question-Answering from Multimodal Talent Tables" (Mannam et al., 22 Jun 2025)
• "DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms" (Bi et al., 5 Mar 2025)
• "Shapley Value-based Contrastive Alignment for Multimodal Information Extraction" (Luo et al., 2024)
• "Prompt-based Multimodal Semantic Communication for Multi-spectral Image Segmentation" (Zhang et al., 25 Aug 2025)
• "Multi-Faceted Multimodal Monosemanticity" (Yan et al., 16 Feb 2025)
• "SemHiTok: A Unified Image Tokenizer via Semantic-Guided Hierarchical Codebook for Multimodal Understanding and Generation" (Chen et al., 9 Mar 2025)
• "Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck" (Cui et al., 2023)
• "Multimodal Graph-Based Variational Mixture of Experts Network for Zero-Shot Multimodal Information Extraction" (Zhou et al., 21 Feb 2025)
• "A Web Scale Entity Extraction System" (Cai et al., 2021)
• "LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing" (Wang et al., 2024)
• "Multi-Modal Coreference Resolution with the Correlation between Space Structures" (Zheng et al., 2018)
• "Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis" (Hu et al., 2023)
• "Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network" (Yang et al., 2017)