Cross-modal Data Generation & Feature Fusion

Updated 10 December 2025

CDGF is a framework that synthesizes and fuses data from heterogeneous sources (e.g., images, text, LiDAR) to enhance multi-sensor perception.
It employs advanced models like GANs, diffusion models, and transformers to achieve semantic alignment and effective feature integration.
Applications span 3D object detection to medical report generation, demonstrating measurable gains in metrics such as CIDEr, mIoU, and AUROC.

Cross-modal Data Generation and Feature Fusion (CDGF) refers to a set of methodologies and architectures that synthesize, align, and jointly exploit heterogeneous modality data—for example, images, text, LiDAR, radar, or tactile signals—by leveraging deep learning models to enable both data translation (generation from one modality to another) and information fusion (feature integration across modalities) at various representational levels. CDGF provides the foundations for high-fidelity data synthesis, robust multi-sensor perception, and unified representation learning, as evidenced in tasks including automatic report generation, 3D object detection, anomaly localization, and robust video understanding.

1. Core Principles and Definitions

CDGF encompasses two synergistic capabilities: (i) cross-modal data generation (the synthesis of modality B data conditioned on modality A), and (ii) cross-modal feature fusion (the systematic combination of representations from multiple modalities into a unified, information-rich embedding). Architectures implementing CDGF typically address (a) semantic and geometric alignment across modalities, (b) computationally efficient and expressively rich fusion operators, and (c) end-to-end learning objectives enforcing cross-modal consistency. In modern frameworks, CDGF modules are instantiated via diffusion models, adversarial networks, transformer-based attention mechanisms, and shared latent embeddings (Liu et al., 2023, Berian et al., 16 Jan 2025, Zhao et al., 3 Dec 2025, Jia et al., 3 Jun 2024, Guo et al., 12 Sep 2025, Ali et al., 20 Oct 2025, Cai et al., 2021).

State-of-the-art cross-modal data generation strategies include conditional generative models (GANs, Diffusion models), latent translation networks, and encoder-decoder pipelines that couple multiple modalities at the feature or latent code level.

Conditional GANs with Residue-Fusion: The visual-tactile generation approach employs conditional GANs augmented with a Residue-Fusion module, injecting high-level semantic features from a pretrained classifier backbone into the generator latent space. This enables data translation (e.g., from image to tactile spectrogram and vice versa), where fusion of encoder and classifier-derived features improves the realism and class consistency of the generated modality. Feature-matching and perceptual losses further regularize the adversarial training (Cai et al., 2021).
Brownian Bridge Diffusion for Modality Bridging: The MOS framework addresses the optical-SAR gap by training a Brownian-bridge diffusion model, learning to interpolate in feature space between optical and SAR domains. During inference, pseudo-SAR samples conditioned on optical features are synthesized via iterative denoising, filling modality gaps for subsequent fusion (Zhao et al., 3 Dec 2025).
Pseudo-modality Generation for Pre-training: In TUNI, large-scale cross-modal pretraining data is synthesized by applying an RGB-to-thermal image generator on ImageNet, yielding pseudo-thermal images precisely registered to their RGB sources for unified encoder pretraining (Guo et al., 12 Sep 2025).
Latent Space Synthesis for Bi-directional Generation: MAFR creates a compact cross-modal latent vector from 2D and 3D feature maps. Modality-specific decoders, with channel-spatial attention (CBAM), reconstruct both original modalities from this joint embedding, enabling bidirectional cross-modal data recovery (Ali et al., 20 Oct 2025).

Feature fusion in CDGF is realized at varying granularity, from global latent spaces to spatially resolved, pixel/voxel-level integration. Leading methods include:

Cascaded and Pixel-wise Attention: GeminiFusion exemplifies efficient per-pixel cross-modal attention via spatially-aligned patch-wise transformers. Local feature pairs are fused using a relation discriminator to balance self and cross-attention, combined with learnable noise vectors for robust inter-modality mixing. GeminiFusion maintains linear complexity in number of tokens, outperforming both token-exchange and quadratic cross-attention methods (Jia et al., 3 Jun 2024).
Dual-Domain Homogeneous Fusion: DDHFusion fuses LiDAR and image features in both BEV (2D) and sparse voxel (3D) domains. Semantic-aware feature sampling and lift-splat-shot projection generate BEV/voxel representations from images, which are then fused intra- and inter-modally via Mamba state-space blocks. Multi-modal voxel feature mixing further enables fine-grained cross-modal feature refinement for downstream 3D object detection (Hu et al., 12 Mar 2025).
Attention-based Extraction with Gated Fusion: In MedCLIP-driven radiology report generation, attention blocks perform both self-attention (to reinforce intra-modal cues) and cross-attention (to mine informative patterns from retrieved report features), integrating them via a learnable scalar-gated sum for the final fused representation (Han et al., 10 Dec 2024).
Unified Encoder Blocks with Local and Global Fusion: TUNI deploys specialized encoder blocks where cross-modal RGB-thermal features are jointly processed using local (Hamilton product for consistent and absolute difference for distinct cues) and global (cross-attention on pooled modalities) modules, with adaptive cosine similarity gating enhancing local fusion saliency (Guo et al., 12 Sep 2025).
3D Volume Fusion for Cross-View Synthesis: CrossModalityDiffusion constructs geometry-aware 3D feature volumes for each input modality (EO, SAR, LiDAR) in a shared world frame. Feature volumes are overlapped spatially, and at each ray-sampled 3D point, features are averaged to produce a fused representation, which is subsequently rendered and fed into modality-specific diffusion decoders for novel view/image synthesis (Berian et al., 16 Jan 2025).

4. Training Objectives and Loss Formulations

CDGF frameworks employ composite objectives, balancing reconstruction, alignment, and generation fidelity:

Joint Reconstruction and Contrastive Losses: Video-Teller minimizes token-level reconstruction loss for both video and text branches, video–text contrastive loss using "global" tokens, and fine-grained MSE alignment between cascaded Q-Former and text auto-encoder outputs (Liu et al., 2023).
Unsupervised and Self-supervised Losses: MAFR deploys a multi-term composite loss combining zero-normalized sum of squared differences (ZNSSD), smoothness (edge-aware regularization), and census loss across both modal reconstructions, driving the unified latent space to preserve semantics of both modalities (Ali et al., 20 Oct 2025).
Diffusion Objectives: CrossModalityDiffusion (and MOS) train conditional diffusion decoders to denoise a noisy latent towards a target modality, backpropagating through the entire fusion-rendering chain, with optional perceptual or reconstruction losses for enhanced visual faithfulness (Berian et al., 16 Jan 2025, Zhao et al., 3 Dec 2025).
Feature-matching and Perceptual Losses: The visual-tactile GAN framework adds feature-matching loss via discriminator layer statistics and VGG-based perceptual losses, improving content and perceptual similarity in generated outputs (Cai et al., 2021).
Alignment and Fusion Balancing: Fusion weights (e.g., the τ coefficient in MOS or learnable scalars in MedCLIP architectures) are validated to optimize the tradeoff between source and generated/modal representations (Zhao et al., 3 Dec 2025, Han et al., 10 Dec 2024).

5. Empirical Performance and Application Domains

CDGF yields measurable improvements across a spectrum of cross-modal tasks:

Video-to-Text Generation: Fine-grained modality alignment in Video-Teller leads to a 4% CIDEr improvement on MSR-VTT with only 13% extra trainable parameters and zero additional inference cost (Liu et al., 2023).
3D Object Detection: Dual-domain fusion, as in DDHFusion, achieves state-of-the-art mAP/NDS on NuScenes with ablation studies attributing consistent gains to each fusional innovation (Hu et al., 12 Mar 2025).
Medical Report Generation: MedCLIP-based CDGF methods deliver SOTA BLEU and METEOR metrics on IU-Xray, with ablation showing joint retrieval and cross-attention fusion as critical (Han et al., 10 Dec 2024).
Semantic Segmentation and Detection: GeminiFusion improves mIoU by 2–3% over prior fusions on NYUDv2, SUNRGBD, DeLiVER, with linear efficiency and scalable deployment (Jia et al., 3 Jun 2024).
Industrial Anomaly Localization: MAFR attains I-AUROC of 0.972 (MVTec 3D-AD), with component analysis verifying the necessity of multi-term loss and attention-driven fusion (Ali et al., 20 Oct 2025).
Robust Cross-modal Retrieval: MOS's cross-modal generation and fusion reduces the optical-SAR retrieval gap, boosting R1 accuracy by 3–16% depending on modality direction and dataset protocol (Zhao et al., 3 Dec 2025).

6. Challenges, Open Problems, and Future Directions

While CDGF architectures have demonstrated efficacy across a variety of perception, generation, and synthesis tasks, several challenges persist:

Modality alignment remains a central obstacle, especially when data are only weakly paired or label correspondence is imperfect. Feature-matching and metric learning losses only partially address this; architectures leveraging joint-latent or geometry-aware spatial fusion show the most promise.
Efficient scaling is essential, particularly in resource-constrained and real-time applications. Pixel/voxel-wise attention and noise-injected fusion (as in GeminiFusion) present scalable alternatives to traditional quadratic attention, yet patch-level and 3D fusion operators still require significant optimization.
Generalization across unseen modality combinations or domains is underexplored. Architectures such as CrossModalityDiffusion implicitly regularize for consistency by rendering all modalities into a shared world-aligned field, a paradigm potentially extensible to transfer and zero-shot settings.
Downstream evaluation: While most works report standard metrics (CIDEr, mIoU, BLEU, AUROC), there is a need for task-specific, modality-aware performance metrics, such as clinical correctness for medical report generation or embodied utility for robotics.

Ongoing research focuses on developing more expressive, interpretable, and scalable CDGF modules, integrating richer priors (e.g., physics-informed representations), and automating architecture search for multimodal fusion pipelines.

Key References:

"Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling" (Liu et al., 2023)
"CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate Representation" (Berian et al., 16 Jan 2025)
"Integrating MedCLIP and Cross-Modal Fusion for Automatic Radiology Report Generation" (Han et al., 10 Dec 2024)
"MOS: Mitigating Optical-SAR Modality Gap for Cross-Modal Ship Re-Identification" (Zhao et al., 3 Dec 2025)
"GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer" (Jia et al., 3 Jun 2024)
"TUNI: Real-time RGB-T Semantic Segmentation with Unified Multi-Modal Feature Extraction and Cross-Modal Feature Fusion" (Guo et al., 12 Sep 2025)
"2D_3D Feature Fusion via Cross-Modal Latent Synthesis and Attention Guided Restoration for Industrial Anomaly Detection" (Ali et al., 20 Oct 2025)
"Visual-Tactile Cross-Modal Data Generation using Residue-Fusion GAN with Feature-Matching and Perceptual Losses" (Cai et al., 2021)
"Dual-Domain Homogeneous Fusion with Cross-Modal Mamba and Progressive Decoder for 3D Object Detection" (Hu et al., 12 Mar 2025)