Modality Pre-fusion Strategies

• Modality pre-fusion is a process of aligning distinct data modalities through architectural and algorithmic strategies to generate joint representations that amplify useful features while mitigating noise.
• It employs techniques such as early, intermediate, and hybrid fusion, leveraging attention mechanisms and adaptive scheduling to integrate modality-specific details effectively.
• Quantitative evaluations in fields like medical imaging, autonomous driving, and sentiment analysis demonstrate that modality pre-fusion significantly boosts accuracy and robustness.

Modality pre-fusion refers to the architectural and algorithmic strategies for aligning, integrating, and transforming information from distinct data modalities (e.g., image, text, audio, sensor data) prior to downstream reasoning, classification, or generative tasks. The goal is to exploit complementary, redundant, or hierarchical features extracted from each modality to create a joint representation that amplifies relevant information and resolves modality-specific noise or deficiencies. This process typically precedes final decision-making layers and is fundamental in applications ranging from medical imaging and autonomous perception to cross-modal retrieval and semantic understanding.

1. Architectural Paradigms for Modality Pre-fusion

Modality pre-fusion architectures span a spectrum from early, intermediate, to late fusion. Early fusion operates directly on raw or minimally processed data, combining modalities at an initial network stage. This is exemplified by convolutional LSTM networks that fuse audio-visual signals in the first C-LSTM layer, yielding a fused tensor for all subsequent processing steps (Barnum et al., 2020). Here, the fusion occurs at the composite input variable $x_t$, constructed from, for example, an image patch $\mathbf{v}$ and an audio spectrogram value $\mathbf{a}_t$. Immediate fusion has been shown to improve noise robustness and test accuracy, particularly under adverse signal-to-noise ratios.

Intermediate fusion mechanisms, such as the mmFUSION framework for autonomous driving, extract compact latent representations from dedicated modality-specific encoders (e.g., ResNet50-FPN for images, sparse 3D convolutions for LiDAR) and harmonize them via attention-based modules (cross-modality and multi-modality attention layers) prior to the detection head (Ahmad et al., 2023). This approach allows richer cross-modal interplay without the alignment assumptions required by early fusion or the proposal dependency of late fusion.

Hybrid architectures, including transformer-based designs, introduce modular expert layers or shared attention blocks. For example, the Mixture-of-Modality-Experts (MoME) transformer in VLMo replaces the standard FFN with pools of modality-specific and vision-language experts within each transformer block, thus facilitating flexible fusion while sharing self-attention layers across modalities (Bao et al., 2021). Models such as SFusion and Tri-modal Fusion (TriMF) use self-attention and bi-modal fusion modules to robustly integrate any subset of available modalities, handling missing data without zero-padding or synthetic imputation (Liu et al., 2022, Wang et al., 2023).

2. Algorithmic Mechanisms and Attention-based Fusion

Attention mechanisms are central to effective modality pre-fusion. The 3D Convolutional Block Attention Module (3D-CBAM) in MMFNet recalibrates concatenated low-level features from multiple MRI encoders by combining channel-wise and spatial weighting via average, max, and standard deviation pooling, each fed through dedicated MLPs. This two-stage attention recalibrates "what" (informative channels) and "where" (spatial regions) before residual fusion and channel reduction (Chen et al., 2018). Similarly, modality-wise and channel-wise fusion is implemented in CMFusion for hate video detection, using learnable feature scores and gating to modulate relevance across channels and modalities (Zhang et al., 17 May 2025).

Another advanced strategy leverages cross-attention, as in SwinFUSE's Domain Invariance Module (DIM): queries for one modality attend to keys and values from another, enabling bidirectional integration of CT and MRI patches into a modality-agnostic representation. This design captures both domain-invariant and modality-specific cues and enhances generalizability across distributional shifts in medical segmentation (Talasila et al., 21 May 2024). The Alternating Telescopic Displacement (ATD) module (Qin, 13 Jun 2024) exemplifies the use of alternating displacement mappings, rotating and shifting each modality's features into the other's space, followed by expansion and projection for unified, joint-space alignment: $$\begin{aligned} z_1 &= \Theta_{12} \, \hat{f}_2, \quad & g_1 &= \hat{f}_1 + z_1, \ z_2 &= \Theta_{21} \, \hat{f}_1, \quad & g_2 &= \hat{f}_2 + z_2, \ f_{\text{fused}} &= \text{Proj}([g_1; g_2]). \end{aligned}$$

Dynamic, data-adaptive fusion schedules induced by lightweight neural schedulers have also been proposed. In Modality-Aware Adaptive Fusion Scheduling (MA-AFS), gradient norms, entropy-based confidence, and uncertainty estimates serve as pre-fusion cues, which are input to a learnable MLP. The scheduler outputs a fusion weight per instance, effectively modulating each modality's contribution in a differentiable manner (Bennett et al., 15 Jun 2025). This consistently improves robustness to modality corruption and enhances generalization under domain shifts.

3. Training Strategies and Initialization

Initialization strategies significantly enhance pre-fusion efficacy. The self-transfer approach in MMFNet pre-trains each encoder in single-modal settings before transferring their weights to the multi-encoder architecture. This promotes retention of modality-specific details and yields stronger joint representations early in training (Chen et al., 2018). Stagewise pre-training, exemplified by VLMo, involves sequential pre-training of modality-specific experts (vision and text) on large unimodal corpora, followed by joint training on paired data to enable robust fusion even when cross-modal pairs are limited (Bao et al., 2021).

Prompt-based pre-fusion, as in PromptFuse, injects a small set of trainable vectors into the input of a fixed pretrained LLM to bridge vision-language representations. By freezing all but the prompt parameters, this approach achieves modularity and parameter efficiency, especially advantageous for low-resource or highly extensible scenarios (Liang et al., 2022).

4. Robustness to Missing, Noisy, and Misaligned Modalities

A persistent challenge in modality pre-fusion is robustness to incomplete, noisy, or misaligned inputs—this is particularly relevant in real-world deployment (medical, autonomous driving, or media content analysis). The SFusion block natively addresses missing modalities by structuring input as tokens for available modalities only, applying self-attention and weighted softmax combining without zero-padding (Liu et al., 2022). In medical data fusion, TriMF constructs all pairwise fusions with uniform dimensionality, and the final global representation is the sum over active pairs. A contrastive loss regularizes the similarity between tri-modal and bi-modal representations, preventing large performance drops when some modalities are absent at inference (Wang et al., 2023).

Approaches such as CMFusion include adaptive gates that modulate the weight of each modality per sample, while temporal cross-attention recalibrates features using time-synchronized audio and video cues, further enhancing robustness to subtle or temporally spread indicators of hate speech (Zhang et al., 17 May 2025). In recommendation settings, SMORE employs dynamic, frequency-domain filtering to suppress modality-specific and cross-modality noise, leveraging FFT-based fusion to capture over both local and global semantic patterns while minimizing noise amplification (Ong et al., 19 Dec 2024).

5. Quantitative Results and Evaluation

Empirical results across application domains underscore the significance of modality pre-fusion. For NPC segmentation, MMFNet achieves mean Dice Similarity Coefficient (DSC) of 72.38% (±10.99), outperforming patch-based and baseline fusion methods by over 12 percentage points in DSC and providing lower ASD and Hausdorff Distance (Chen et al., 2018). In multimodal sentiment analysis, Bi-Bimodal Fusion Network (BBFN) surpasses previous SOTA on CMU-MOSEI with MAE of 0.529, 7-class accuracy of 54.8%, and binary accuracy of 86.2% (Han et al., 2021).

In 3D object detection on KITTI and nuScenes, intermediate fusion (mmFUSION) consistently yields the highest mAP and NDS metrics compared to early, late, and two-stage fusion schemes (Ahmad et al., 2023). In video content moderation, CMFusion delivers an F1 score of 0.860 (a >5% improvement over baselines) with recall of 0.908, crucial for minimizing false negatives (Zhang et al., 17 May 2025). Multimodal recommendation with SMORE yields consistent gains on Recall@10 and NDCG@10 across large real-world e-commerce datasets, attributing improvement to spectral domain fusion and graph-based propagation (Ong et al., 19 Dec 2024).

Ablation studies frequently highlight the necessity of both modality-alignment modules and fusion blocks; removing or simplifying these components typically leads to measurable performance drops. The combination of modality-aware, data-driven fusion, robust initialization, and attention-based recalibration is validated across domains.

6. Implications, Limitations, and Future Directions

Modality pre-fusion has broad implications for the design of scalable, robust, and generalizable multimodal systems. Architectures that efficiently balance modality-specific and cross-modal representations enable improved handling of noise, missing data, or distributional shifts—critical for medical diagnostics (Talasila et al., 21 May 2024, Wang et al., 2023), security (Farhadipour et al., 31 Aug 2024), and autonomous systems (Sun et al., 26 Sep 2024). Adaptive fusion mechanisms confer further robustness, as demonstrated by MA-AFS' resilience under input corruption and domain shifts (Bennett et al., 15 Jun 2025).

Open challenges persist: pre-fusion strategies may incur 1–2% performance deficits in in-domain scenarios compared to modality-specialized models (Talasila et al., 21 May 2024); fusion redundancy can occur if independently pre-trained encoders are loaded without adaptation (Sun et al., 26 Sep 2024). Advanced pruning (AlterMOMA) and fusion gating may mitigate these issues, but architectural innovations are required for modalities with highly divergent statistical properties or for real-time, scalable deployment under non-ideal conditions.

Future research directions include the expansion to additional modalities (e.g., structured data, video, sensor streams), refinement of adaptive scheduling and attention, and the development of self-supervised or unsupervised pre-fusion training regimes that further lower supervision requirements and enhance cross-domain generalization. As modality pre-fusion principles become foundational to multimodal foundation models, their rigorous evaluation and principled design remain central priorities in the advancement of robust AI systems.