Cross-modal Prompting (ComP) Overview

Updated 16 December 2025

Cross-modal Prompting (ComP) is a technique that creates shared prompt representations from heterogeneous modalities, enhancing inter-modal synergy and data robustness.
It mitigates modality imbalance and boosts performance in tasks such as emotion recognition, visual segmentation, and continual learning.
The approach employs dynamic modules, attention-based prompt fusion, and progressive training strategies to optimize multi-modal data integration.

Cross-modal Prompting (ComP) refers to a family of techniques that enhance multi-modal learning by using prompt-style representations to facilitate information exchange, fusion, and adaptation among heterogeneous data modalities (e.g., audio, video, text, or image), especially in transformer-based or large pre-trained neural architectures. Unlike classical fusion (late or early) and unimodal prompting, cross-modal prompting architectures explicitly construct, inject, and propagate prompt representations synthesized from or informed by multiple modalities, thereby mitigating modality imbalance, improving robustness to missing data, and promoting deep inter-modal synergy.

1. Conceptual Foundations and Motivation

Cross-modal prompting methods are motivated by two persistent challenges in multi-modal learning. First, the performance gap and suboptimal optimization of weaker modalities often lead to modality domination, where one view suppresses the others, reducing robustness, especially with incomplete (missing) data. Second, classical fusion architectures and isolated prompt pipelines fail to leverage coherent information that emerges only through cross-modal exchange (He et al., 12 Dec 2025).

The central insight underlying ComP is that constructing and broadcasting compact, consensus-oriented prompt representations—derived by learning from one or more modalities and delivered into others—can (a) directly enhance each modality’s discriminative capacity, (b) dynamically balance contributions during fusion, and (c) facilitate graceful degradation (and even improvement) in the presence of incomplete, missing, or noisy data.

This paradigm has proven highly effective in applications such as incomplete multi-modal emotion recognition (He et al., 12 Dec 2025), robust visual recognition with missing modalities (Zhang et al., 10 Jul 2025), domain-incremental continual learning (Feng et al., 22 Jul 2024, Wang et al., 24 Jun 2025), and medical image translation and segmentation (Chen et al., 2023, Yu et al., 29 Jun 2025).

2. Representative Architectures and Prompt Generation Mechanisms

Cross-modal prompting implementations vary across tasks, but common architectural elements have emerged:

2.1. Prototype-Based Prompt Generation

In incomplete multi-modal emotion recognition, a progressive prompt generation module first distills batch-wise features for each modality into a compressed set of prototypes (via an MLP) encoding shared “emotion-consistent” cues. Subsequent blocks auto-regressively update the prompt stream with (i) similarity-based soft assignment against the prototypes, (ii) momentum-style fusion for temporal stability, and (iii) sample-adaptive gradient modulation to redirect learning focus toward harder (under-optimized) modalities (He et al., 12 Dec 2025).

Multiple modalities’ features and prompt representations are repeatedly fused and propagated through blocks using concatenation, linear projection, and masked multi-head self-attention. For each modality, features are concatenated with prompts received from the other modalities, passed through attention, and then re-split to renew features and cross-modal prompts. This process systematically injects consensus information and amplifies cross-modal consistency within each modality’s output (He et al., 12 Dec 2025).

2.3. Coordinator Modules and Fusion

After cross-modal propagation, a dynamic coordinator MLP computes per-sample weights for each modality’s enhanced features, enabling balanced, context-adaptive fusion before final classification/regression (He et al., 12 Dec 2025). Layer-wise propagation of prompts, as in synergistic prompting (Zhang et al., 10 Jul 2025), ensures information is refined at multiple processing depths.

2.4. Application-Specific Variants

Dynamic adapters: Generate scaling factors to modulate base prompts on a per-sample basis, accommodating arbitrary missing data patterns (Zhang et al., 10 Jul 2025).
Hierarchical prompt blocks: At each encoder/decoder level, prompt embeddings are extracted and fused with activations via Transformer blocks, supporting multi-task image translation (Chen et al., 2023).
Attention-based prompt fusion: Cross-modal prompt attention (CMPA) modules mediate interaction between visual/textual prompts at each depth, as in deeply coupled prompt learning (Liu et al., 2023).

3. Methodological Innovations and Training Strategies

Cross-modal prompting frameworks typically adopt hybrid or progressive training strategies:

3.1. Progressive/Two-Stage Training

Stage 1: Each modality encoder is pre-trained or adapted on its modality alone (including zero-imputation for missing data).
Stage 2: Encoders are (optionally) frozen; cross-modal prompting modules (PG/KP/Coordinator) are inserted, trained solely on downstream tasks (e.g., emotion classification, sentiment regression), using task loss on fused outputs (He et al., 12 Dec 2025).

No explicit reconstruction loss is needed; cross-modal knowledge propagation reconstructs missing/ambiguous tokens implicitly.

3.2. Prompt Optimization and Regularization

Sample-wise gradient modulation explicitly retards learning in “easy” modalities to reallocate capacity to under-optimized ones (He et al., 12 Dec 2025).
Cross-modal alignment losses (contrastive, distillation) enforce semantic correspondence and prevent representational drift (Li et al., 26 May 2025, Han et al., 2022).

3.3. Dynamic Prompting and Recovery

Prompt query selection incorporates context from the opposite modality, inhibiting prompt isolation (Li et al., 26 May 2025).
Prompt recovery leverages cross-modal attention to reconstruct masked segments, promoting deeper semantic fusion and both intra- and inter-modal robustness (Li et al., 26 May 2025).

Rigorous validation across disciplines demonstrates both state-of-the-art performance and robustness of cross-modal prompting strategies under missing, incomplete, or imbalanced modality conditions.

Example Results

Benchmark / Task	SOTA/Best Prior	ComP Accuracy / Gain
CMU-MOSI @ MR=0.3 (emotion, 3-modal)	ACC 81.20	ACC 83.69 (+2.5); F1 83.67 vs 81.0
MM-IMDb (F1-Macro, missing modalities)	MgCoOp: <77	SyP >+2–3 F1-Macro abs. over baseline
CDDB-Hard CL (2 dom., 50c)	88.79	93.65 (+4.86), Fgt −0.25
AVSBench-Objects (audio-visual seg.)	85.77 mIoU	87.64 mIoU (+1.87)

Results consistently show that (a) accuracy and F1 gains persist even with high (up to 70%) missing rates, (b) all single-modal branches are improved after cross-modal prompting, and (c) ablation of key modules (knowledge propagation, prompt generation, coordinator) degrades or collapses performance, confirming the necessity of the cross-modal design (He et al., 12 Dec 2025, Zhang et al., 10 Jul 2025, Feng et al., 22 Jul 2024, Chen et al., 7 Jul 2024).

Cross-modal prompting stands in contrast to previous strategies:

Classical fusion: Standard early/late/unified fusion often lets one modality dominate and does not address data missingness or modality under-optimization (He et al., 12 Dec 2025).
Naive prompt-based tuning: Static, uni-modal, or shallow prompt strategies lack adaptation to varying data patterns and do not balance robustly in missing-modality situations (Zhang et al., 10 Jul 2025).
Replay- or buffer-based continual learning: ComP methods such as CP-Prompt achieve strong domain adaptation and knowledge retention without any past-data replay or weight distillation (Feng et al., 22 Jul 2024, Wang et al., 24 Jun 2025), due to “shallow” common and “deep” personalized prompts.

Notably, layer-deep architectures incorporating value-channel prompt injections, dynamic prompt selection, and cross-modal prompt attention bring substantial performance benefits over shallow or single-modal tuning (Liu et al., 2023, Wang et al., 24 Jun 2025).

6. Ablation Studies and Mechanistic Insights

Ablations isolate the contribution of each design element:

Disabling cross-modal knowledge propagation collapses performance to or below unimodal baselines.
Random or fixed (non-progressive) prompts yield 1–2% lower accuracy and reduced robustness.
Removal of dynamic gradient or alignment mechanisms especially impairs regression or continual learning tasks.
Visualization via t-SNE and attention maps reveals that cross-modal prompting enhances cluster separability and focuses attention on semantically aligned, multi-modal regions (He et al., 12 Dec 2025, Li et al., 26 May 2025).
Domain-adaptive or context-aware prompt selection—employing hybrid common/personalized banks, prototype-extractor matching, or dynamic query fusion—ensures optimal prompt activation at inference and resists catastrophic forgetting (Feng et al., 22 Jul 2024, Wang et al., 24 Jun 2025).

7. Limitations, Extensions, and Future Directions

Residual limitations include reliance on basic domain selectors (e.g., K-means) for prompt adaptation in highly overlapping or high-cardinality regimes (Feng et al., 22 Jul 2024). In very high missingness or extreme domain drift settings, further improvements may require more advanced cross-modal distillation or adaptive prompt regularization.

Promising directions for extension include: cross-modal prompt generalization to other foundation models (speech–vision, vision–robotics), unsupervised prompt refinement, more sophisticated alignment losses, lightweight or human-in-the-loop prompt design, and scalable pre-training on larger, more varied multimodal datasets (Chen et al., 2023, Zhang et al., 10 Jul 2025). There is also active exploration into compositional and partitioned prompt architectures to support hierarchical or highly structured cross-modal adaptation (Tian et al., 2023).

A plausible implication is that cross-modal prompting strategies, by formalizing explicit information interchange and adaptation among modalities, will become a core component for robust, scalable, and efficient multi-modal foundation models addressing real-world, heterogeneous, and incomplete data scenarios across vision, language, audio, and biomedical domains (He et al., 12 Dec 2025, Wang et al., 24 Jun 2025).