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Multimodal Structured Generation: CVPR's 2nd MMFM Challenge Technical Report

Franz Louis Cesista

TL;DR

This work tackles the challenge of deep multimodal integration in document understanding by constraining frozen multimodal foundation models to produce outputs in a predefined structure through hard logit constraints, enabling parseable results without costly fine-tuning. By extending the Retrieval Augmented Structured Generation idea to multimodal models, the authors demonstrate that forcing the model to reason before answering yields competitive results in the CVPR 2nd MMFM Challenge, achieving 2nd place in Phase 2 and 3rd overall while using only lightweight engineering. The approach emphasizes reproducibility and low computational overhead, and shows that, for key-information extraction tasks, vision cues may be less critical than accurate information extraction from text with proper prompting and structure. Overall, the work suggests a practical, scalable direction for deploying document-understanding systems that require tight data formats and minimal fine-tuning, with demonstrated generalization to unseen datasets.

Abstract

Multimodal Foundation Models (MMFMs) have demonstrated strong performance in both computer vision and natural language processing tasks. However, their performance diminishes in tasks that require a high degree of integration between these modalities, such as document understanding. Moreover, finetuning these models and deploying them requires significantly more compute and more engineering effort than unimodal models. In this work, we present Multimodal Structured Generation, a framework that forces (frozen) MMFMs to produce outputs in a strictly structured format by applying hard constraints directly to the output logits. This approach not only ensures that the model generates parseable outputs that downstream APIs can easily ingest but also allows us to force the model to reason before answering, which significantly boosts performance without the need for expensive fine-tuning. We demonstrate the effectiveness of our method through competitive results in the CVPR 2nd MMFM Challenge, highlighting that carefully designed lightweight engineering can outperform expensive and complicated modeling approaches. All of our scripts, deployment steps, and evaluation results can be accessed in https://github.com/leloykun/MMFM-Challenge

Multimodal Structured Generation: CVPR's 2nd MMFM Challenge Technical Report

TL;DR

This work tackles the challenge of deep multimodal integration in document understanding by constraining frozen multimodal foundation models to produce outputs in a predefined structure through hard logit constraints, enabling parseable results without costly fine-tuning. By extending the Retrieval Augmented Structured Generation idea to multimodal models, the authors demonstrate that forcing the model to reason before answering yields competitive results in the CVPR 2nd MMFM Challenge, achieving 2nd place in Phase 2 and 3rd overall while using only lightweight engineering. The approach emphasizes reproducibility and low computational overhead, and shows that, for key-information extraction tasks, vision cues may be less critical than accurate information extraction from text with proper prompting and structure. Overall, the work suggests a practical, scalable direction for deploying document-understanding systems that require tight data formats and minimal fine-tuning, with demonstrated generalization to unseen datasets.

Abstract

Multimodal Foundation Models (MMFMs) have demonstrated strong performance in both computer vision and natural language processing tasks. However, their performance diminishes in tasks that require a high degree of integration between these modalities, such as document understanding. Moreover, finetuning these models and deploying them requires significantly more compute and more engineering effort than unimodal models. In this work, we present Multimodal Structured Generation, a framework that forces (frozen) MMFMs to produce outputs in a strictly structured format by applying hard constraints directly to the output logits. This approach not only ensures that the model generates parseable outputs that downstream APIs can easily ingest but also allows us to force the model to reason before answering, which significantly boosts performance without the need for expensive fine-tuning. We demonstrate the effectiveness of our method through competitive results in the CVPR 2nd MMFM Challenge, highlighting that carefully designed lightweight engineering can outperform expensive and complicated modeling approaches. All of our scripts, deployment steps, and evaluation results can be accessed in https://github.com/leloykun/MMFM-Challenge
Paper Structure (11 sections, 1 figure, 2 tables)

This paper contains 11 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Multimodal Structured Generation
  4. Implementation Details
  5. Datasets
  6. Results
  7. Discussion
  8. Conclusion
  9. JSON Output Formats
  10. JSON output format for the myinfographic and mychart datasets
  11. JSON output format for the mydoc datasets

Figures (1)

  • Figure 1: Multimodal Structured Generation