Modality-Specific Encoder Architectures

• Modality-specific encoder architectures are specialized neural models that tailor processing to distinct data types, ensuring optimal representation for each modality.
• They employ techniques like parallel independent encoders, hierarchical fusion, and mixture-of-experts routing to preserve intra-modality integrity while enabling cross-modal integration.
• Empirical studies demonstrate improved performance across tasks such as sentiment analysis, visual question answering, and medical imaging through validated design tradeoffs and sample-efficient adaptation.

Modality-specific encoder architectures are specialized neural models designed to process data from distinct input modalities (e.g., text, vision, audio, events, multimodal biosignals), where each encoder is tailored to capture the statistical structure and inductive biases unique to its input type. These architectures are a foundational component of modern multi-modal and cross-modal learning systems, enabling efficient representation, robust fusion, and effective transfer or adaptation for diverse downstream tasks.

1. Architectural Principles of Modality-Specific Encoders

Modality-specific encoders operate on the principle of architectural specialization: for each input type, an encoder is constructed or selected to optimally process the data distribution and structural properties of that modality. For vision, encoders commonly use deep convolutional backbones or vision transformers; for language, transformer-based models with token- and position-aware embeddings dominate; for audio, deep 1D CNNs and transformer encoders over spectrograms or waveform patches are prevalent; for event data, models adopt mapping schemes compressing spatiotemporal tuples into static representations compatible with visual encoders.

Several archetypal designs have emerged:

• Separate but Parallel Encoders: Each modality is assigned a fully independent encoder (possibly pre-trained on large unimodal data), producing representations later combined or fused. This is prevalent in multimodal sentiment analysis pipelines employing, e.g., CLIP-ViT for vision, WavLM for audio, and BERT for text, with all weights frozen and only the fusion layers trained for downstream tasks (Ando et al., 2022).
• Hierarchical Modular Encoders and Fusion: Architectures such as LXMERT deploy independent encoders for vision and language, followed by a specialized cross-modality encoder that interleaves modality-specific self-attention with bi-directional cross-attention, preserving intra-modality reasoning before semantic alignment (Tan et al., 2019).
• Mixture-of-Expert Systems: Mixture schemas are employed where multiple expert encoders, each specialized to subdomains or data types within a modality, are dynamically selected via a routing mechanism. For example, MOVE routes each input image through one of several expert vision encoders (e.g., InternViT, Texify, UniChart) chosen by a lightweight router MLP for domain-sensitive processing, thus outperforming single-encoder baselines in domain-diverse tasks (Skripkin et al., 21 Feb 2025).
• Expert Pools within Unified Architectures: The VLMo architecture builds mixture-of-modality-experts (MoME) at the Transformer block level, swapping in expert FFN submodules dependent on token modality, while keeping multi-head self-attention layers shared. This modularity enables efficient switching between dual-encoder and fusion-encoder functional regimes (Bao et al., 2021).
• Layer-Specific Modality Specialization: In certain late-fusion designs, representations are extracted from intermediate or penultimate layers, as these layers often retain richer semantic features for the downstream task than the heavily pretraining-specialized final layers (Ando et al., 2022).

2. Specialization and Fusion Mechanisms

A critical distinction in modality-specific architectures is the treatment of intra-modality versus cross-modality processing. Representative strategies include:

• Dedicated Early Processing: Each encoder processes its raw input using modality-tuned layers (e.g., CNN for images, Transformer for text/audio), allowing full exploitation of intra-modality dependencies. For example, LXMERT's object-relationship encoder represents each detected object through concatenated RoI features and bounding box coordinates projected to a shared space (Tan et al., 2019).
• Controlled Fusion: Fusion is often intentionally delayed until intra-modality context has been developed, then achieved via explicit cross-attention, concatenation, gating, or variational fusion mechanisms. LXMERT interleaves modality-specific self-attention with bi-directional cross-attention at the cross-modality encoder stage, minimizing over-mixing and preserving semantic structure (Tan et al., 2019).
• Adaptive Routing and Mixture-of-Experts: MOVE's mixture-of-encoders design uses a router MLP to select a vision encoder on a per-sample basis, which is highly effective when inputs are heterogeneous or domain-structured (Skripkin et al., 21 Feb 2025).
• Expert Gating Within Layers: VLMo's MoME transformer applies hard gating at each transformer block, routing tokens to expert FFN submodules depending on their modality and sequence context (Bao et al., 2021).
• Share vs. Specialize Tradeoffs: MS-CLIP ablation studies demonstrate that sharing attention and FFN weights across vision and language—while keeping input/output projections and LayerNorms modality-specific—yields superior zero-shot and linear probing accuracy, while more aggressive parameter sharing (sharing LNs) or partial sharing underperforms (You et al., 2022).

3. Modular Encoder Instantiations Across Modalities

Distinct modalities frequently require specialized encoder instantiations. Key examples across domains include:

Modality	Encoder Architecture	Notable Example
Vision	ViT, ConvNet, MoME block	CLIP-ViT, InternViT, VLMo E_v
Text	Transformer, BERT-like models	BERT, VLMo E_ℓ
Audio	CNN + Transformer	WavLM, Cacophony
Event	CLIP-adapted ViT (1-channel)	Robust CLIP-Based Event Encoder
Multimodal MRI	Per-modality 3D U-Net towers	U-HVED, FedMEMA
Specialized Vision	Domain experts (chart, document)	UniChart, Texify, MOVE
General-purpose	Expert pool + router	MOVE, VLMo

Notably, in U-HVED and FedMEMA, independent 3D U-Net-style encoders are deployed for each MRI modality (T1, T1c, T2, FLAIR), with separate weights and normalization, thus capturing non-stationary distributions and acquisition-specific contrast (Dorent et al., 2019, Dai et al., 2024).

For low-resource or emerging modalities, as in SEMI, arbitrary encoder architectures can be integrated by sample-efficiently learning adapters to align their representations with the LLM’s embedding space, using a hypernetwork trained on high-resource modalities and support examples, enabling integration at vastly reduced data requirements (İnce et al., 4 Sep 2025).

4. Integration and Fusion Strategies with Modality-Specific Encoders

How the outputs of modality-specific encoders are fused is a pivotal aspect affecting system performance and flexibility.

• Cross-Modality Encoders: LXMERT deploys a cross-modality encoder that alternates intra-modality self-attention and cross-modal bi-directional attention, closely integrating visual and linguistic features at multiple levels (Tan et al., 2019).
• Late Fusion with Gating: In large-scale sentiment analysis, encoder outputs are pooled into fixed-size vectors per modality and fused via a trainable gated decoder, which learns the optimal weighting scheme for each fused representation (Ando et al., 2022).
• Parallel and Adapter Paths: MS-CLIP introduces lightweight vision- or text-specific parallel modules (e.g., residual CNNs for early vision specialization) feeding into a shared backbone, which is shown in ablations to further improve performance over a purely shared or purely separated setup (You et al., 2022).
• Mixture-of-Experts Routing and Adapterization: MOVE routes inputs through a selected expert encoder with domain-matched adapter projections before concatenation with language features and feeding into an LLM, preserving computational efficiency as only one vision encoder is active per input (Skripkin et al., 21 Feb 2025).
• Cross-stitching via Cross-Attention: In audio-text settings, individual encoders are connected via a two-way multi-head cross-attention module, stitched at the token/frame level, yielding gains over both unimodal and simple concatenation baselines (Singla et al., 2022).
• Variational Fusion: The hetero-modal variational encoder-decoder (U-HVED) fuses per-modality latent Gaussian embeddings via a product-of-Gaussians junction, enabling flexible inference with arbitrary observed subsets and principled handling of missing data (Dorent et al., 2019).

5. Training, Adaptation, and Personalization

Modality-specific architectures naturally support efficient pretraining, adaptation, and downstream personalization, with modality specialization facilitating modularity and privacy:

• Stagewise Pretraining: VLMo leverages sequential stagewise pretraining, initializing modality-specific experts on large image-only (BEiT) and text-only (BERT) datasets, then fine-tuning on paired image-text data to maximize sample coverage and utilization of unimodal resources (Bao et al., 2021).
• Sample-Efficient Adapterization: SEMI achieves efficient integration of new, potentially low-resource modalities into LLMs by generating adapters (via a meta-learned hypernetwork) that need only a handful of paired examples to calibrate a new encoder's output space, attaining 16–64× higher sample efficiency than training a mapping from scratch (İnce et al., 4 Sep 2025).
• Federated and Personalized Training: In federated medical imaging setups, FedMEMA enables both global training and personalized local adaptation by sharing only encoder parameters (one per modality) and multi-modal anchors. Decoders are personalized and fusion is achieved via cross-attention to global multimodal anchors, allowing monomodal clients to benefit from multimodal statistics without data sharing (Dai et al., 2024).

6. Empirical Performance and Design Tradeoffs

A broad array of empirical results indicate:

• Modality-specific encoders, especially when large-scale pre-trained, consistently outperform conventional heuristic or single-backbone features in both unimodal and multimodal settings for sentiment analysis, VQA, segmentation, and classification (Ando et al., 2022, Skripkin et al., 21 Feb 2025, Tan et al., 2019, Bao et al., 2021).
• Late-fusion architectures with gated decoders are robust and effective, with ablations showing best sentiment regression performance when fusing intermediate representations from each encoder (Ando et al., 2022).
• Full separation versus parameter sharing: MS-CLIP demonstrates that sharing all Transformer layers except LayerNorms across vision and language yields higher zero-shot accuracy with fewer parameters than separate or partially shared designs (You et al., 2022).
• In mixture-of-encoders systems (e.g., MOVE), specialized encoders routed by an efficient gating network outperform both single-encoder and slicing-based approaches across domain-diverse benchmarks, especially for high-resolution visual inputs and structured document/graph/image types (Skripkin et al., 21 Feb 2025).
• In medical imaging, per-modality encoders plus fusion outperform single-branch architectures by 12–16 percentage points in monomodal Dice score, with additional gains from cross-attention calibration to multimodal anchors (Dai et al., 2024).
• For low-resource domain adaptation, meta-learned hypernetworks for per-encoder adapter generation are orders of magnitude more sample-efficient than projector training from scratch, closing performance gaps with few labeled examples (İnce et al., 4 Sep 2025).

7. Modality-Specific Encoders in Broader Multimodal System Design

Modality-specific encoder architectures serve as the backbone for a spectrum of multi-modal, cross-modal, and retrieval frameworks, supporting crucial capabilities:

• Enabling flexible system composition (“plug-and-play” encoders, e.g., for event, depth, text, and audio (Jeong et al., 2024)).
• Facilitating privacy-aware and federated scenarios by maintaining separated encoder weights and modular training/fusion (Dai et al., 2024).
• Supporting scalable domain extension via sample-efficient adaptation with no need for foundational model retraining (İnce et al., 4 Sep 2025).
• Enhancing learning dynamics by enabling pretraining, ablation, and modularity across diverse input domains—empowering researchers to tailor systems precisely for tasks as varied as VQA, document understanding, cross-modal retrieval, affect recognition, and medical image segmentation.

Taken together, modality-specific encoder architectures represent a principled and empirically validated paradigm for constructing robust, extensible, and high-performing multimodal learning systems. Their design continues to evolve with advances in foundation model training, efficient adaptation, and federated learning techniques, offering a blueprint for scalable integration of ever-broadening modality sets.