How Far Are We from Generating Missing Modalities with Foundation Models?

Abstract: Multimodal foundation models have demonstrated impressive capabilities across diverse tasks. However, their potential as plug-and-play solutions for missing modality reconstruction remains underexplored. To bridge this gap, we identify and formalize three potential paradigms for missing modality reconstruction, and perform a comprehensive evaluation across these paradigms, covering 42 model variants in terms of reconstruction accuracy and adaptability to downstream tasks. Our analysis reveals that current foundation models often fall short in two critical aspects: (i) fine-grained semantic extraction from the available modalities, and (ii) robust validation of generated modalities. These limitations lead to suboptimal and, at times, misaligned generations. To address these challenges, we propose an agentic framework tailored for missing modality reconstruction. This framework dynamically formulates modality-aware mining strategies based on the input context, facilitating the extraction of richer and more discriminative semantic features. In addition, we introduce a self-refinement mechanism, which iteratively verifies and enhances the quality of generated modalities through internal feedback. Experimental results show that our method reduces FID for missing image reconstruction by at least 14\% and MER for missing text reconstruction by at least 10\% compared to baselines. Code are released at: https://github.com/Guanzhou-Ke/AFM2.

• The paper presents three paradigms for missing modality generation, emphasizing direct generation, filtering, and mining approaches.
• It introduces an innovative agentic framework that combines miner, verifier, and generator agents with a self‐refinement mechanism.
• Empirical results on datasets like VGGSound and MSRVTT show improved semantic consistency, though audio generation remains challenging.

Analysis of "How Far Are We from Generating Missing Modalities with Foundation Models?"

This essay critically evaluates the paper titled "How Far Are We from Generating Missing Modalities with Foundation Models?" (2506.03530). The paper addresses the emerging need for effective solutions to missing modality reconstruction in multimodal systems, leveraging foundation models. The study explores three paradigms for generating missing modalities, emphasizing the limitations of current models and proposing an innovative agentic framework to enhance performance.

Introduction to Missing Modality Generation

Missing modalities present a significant challenge in multimodal systems, often arising from technical limitations or privacy concerns. Existing methodologies primarily focus on semantic representation recovery, neglecting raw data reconstruction which is critical for applications demanding precise modality inputs, such as image-conditioned generation or speech synthesis. The paper identifies and categorizes three paradigms for missing modality reconstruction that hinge on leveraging foundation models without necessitating fine-tuning for each downstream task.

Figure 1: Overview of three paradigms for missing modality generation.

Paradigms for Missing Modality Generation

The paper delineates three distinct paradigms:

1. Direct Generation: This paradigm utilizes models to directly generate missing modalities from available inputs, such as leveraging Stable Diffusion for text-to-image generation. The absence of a validation mechanism in this approach often leads to semantically inconsistent outputs.
2. Generation with Filter: Here, multiple candidate outputs are generated, among which the most semantically consistent candidate is selected using a filtering model like ImageBind.
3. Generation with Miner and Filter: This advanced paradigm integrates a cross-modal miner that extracts and integrates multimodal knowledge, followed by filtering to select the optimal output. This method aims to enhance semantic alignment and output fidelity.

Evaluating Paradigm Effectiveness

The empirical evaluation conducted spans 42 variants across these paradigms, measuring performance using a range of datasets (e.g., VGGSound, MSRVTT). Key metrics include FID and CLIP-I for image, MER and CLIP-T for text, and PESQ and SI-SNR for audio. Results demonstrate enhancements in generation quality with integrated filtering and mining. However, audio generation lags significantly, attributed to its inherent complexity and the over-reliance on text-based prompts.

Figure 2: The major quantitative results of the three paradigms across four datasets.

Proposed Agentic Framework and Self-Refinement Mechanism

To address identified challenges, the authors propose an Agentic Framework for Missing Modality (AFM$^2$) that amalgamates three agents: miner, verifier, and generator. The miner dynamically extracts fine-grained elements from observed modalities. The verifier ensures semantic consistency through iterative feedback. The generator adapts its approach according to refined guidance, aiming to produce higher-quality outputs more efficiently.

Figure 3: Overview of an agentic framework for generating missing modalities.

A significant innovation in AFM$^2$ is the self-refinement mechanism that iteratively refines candidate generations to meet semantic quality thresholds. This dual focus on quality and efficiency highlights a robust advancement over existing methods.

Figure 4: Impact of self-refinement rounds and generation threshold values on the quality of missing modality generation.

Implications and Future Directions

The study underscores significant implications for both practical applications and theoretical advancements. The agentic approach enhances adaptability in diverse and challenging environments such as healthcare, where data completeness is pivotal. Future research should focus on extending these frameworks to encompass additional modalities like video and exploring training-efficient adaptations such as LoRA and prompt tuning, as well as fostering joint reasoning capabilities within generation methodologies.

Conclusion

The paper offers a comprehensive examination of the current landscape and capabilities of foundation models in missing modality generation, identifying critical limitations and proposing a sophisticated framework to overcome them. AFM$^2$ represents a meaningful stride towards more versatile and efficient multimodal systems, promising to greatly impact a variety of application domains.