CompCap: Improving Multimodal Large Language Models with Composite Captions (2412.05243v1)

Abstract: How well can Multimodal LLMs (MLLMs) understand composite images? Composite images (CIs) are synthetic visuals created by merging multiple visual elements, such as charts, posters, or screenshots, rather than being captured directly by a camera. While CIs are prevalent in real-world applications, recent MLLM developments have primarily focused on interpreting natural images (NIs). Our research reveals that current MLLMs face significant challenges in accurately understanding CIs, often struggling to extract information or perform complex reasoning based on these images. We find that existing training data for CIs are mostly formatted for question-answer tasks (e.g., in datasets like ChartQA and ScienceQA), while high-quality image-caption datasets, critical for robust vision-language alignment, are only available for NIs. To bridge this gap, we introduce Composite Captions (CompCap), a flexible framework that leverages LLMs and automation tools to synthesize CIs with accurate and detailed captions. Using CompCap, we curate CompCap-118K, a dataset containing 118K image-caption pairs across six CI types. We validate the effectiveness of CompCap-118K by supervised fine-tuning MLLMs of three sizes: xGen-MM-inst.-4B and LLaVA-NeXT-Vicuna-7B/13B. Empirical results show that CompCap-118K significantly enhances MLLMs' understanding of CIs, yielding average gains of 1.7%, 2.0%, and 2.9% across eleven benchmarks, respectively.

• The paper introduces CompCap, a novel framework that automates the generation of composite image-caption pairs to address limitations in processing detailed composite images.
• It demonstrates that fine-tuning three MLLMs with the new CompCap-118K dataset improves performance on composite image benchmarks by 1.7%, 2.0%, and 2.9%.
• The study highlights the importance of high-quality, diversified caption data in enhancing vision-language alignment, with practical implications for document analysis and UI screenshot interpretation.

An Analysis of #CompCap: Enhancing Multimodal LLMs via Composite Captions

The paper "#CompCap: Improving Multimodal LLMs with Composite Captions" offers a comprehensive investigation into the limitations of existing Multimodal LLMs (MLLMs) when processing composite images (CIs). These CIs include a variety of detailed visual elements such as charts, collages, and infographics, synthesized from different media rather than captured naturally. This paper identifies a significant gap in existing MLLMs, which primarily focus on natural images (NIs) and do not effectively handle the complexities presented by CIs.

The researchers introduce a new framework, known as CompCap, designed to automate the generation of CI-caption pairs to address this deficiency. By synthesizing CIs along with accurate and comprehensive captions, CompCap generates a dataset labeled CompCap-118K, comprising 118,000 image-caption pairs across six different types of CIs. This dataset significantly increases the breadth and quality of training data available for MLLMs.

Numerical Insights and Observations

The paper provides quantifiable improvements achieved through the introduction of CompCap. Three different MLLMs—xGen-MM-inst.-4B, LLaVA-NeXT-Vicuna-7B, and LLaVA-NeXT-Vicuna-13B—were fine-tuned using CompCap-118K. These models demonstrated performance improvement across eleven benchmarks specific to CIs, with gains of 1.7%, 2.0%, and 2.9%, respectively.

Furthermore, detailed experiments illustrated that high-quality captions directly enhance MLLMs’ understanding of CIs. A specific ablation paper, breaking down contributions from each CI category, reaffirmed that each category helps improve MLLM performance. Analysis showed that the inclusion of caption data highly enhances vision-language alignment, compared to traditional instruction-following data, which mostly improves conversational capabilities.

Theoretical and Practical Implications

From a theoretical perspective, the paper underscores the necessity for diversified and high-quality datasets that accommodate the unique intricacies of CIs. It challenges conventional practices focusing predominantly on NIs, advocating for an enriched training dataset that encompasses varied CI forms. This necessitates a shift in dataset curation priorities to improve the robustness and versatility of MLLMs.

Practically, the findings of this research indicate potential applications in fields that heavily rely on composite image understanding, such as document analysis, complex diagram interpretation, and user-interface screenshots processing. Improved CI comprehension in MLLMs could enable advanced applications in automated reporting, educational tools, and intelligent virtual assistants, thereby potentially enhancing user interaction and accuracy in information dissemination.

Future Directions

The development of the CompCap framework and the subsequent CompCap-118K dataset present numerous avenues for further exploration. Future research could involve expanding the diversity of CI types within the dataset or increasing the scale to further enhance model generalization. Additionally, integrating CI data into MLLMs for domain-specific applications, such as medical imaging or geological mapping, could reveal further insights into specific patterns and trends.

Moreover, exploring hybrid model architectures optimized for CI complexities and potentially leveraging attention mechanisms tailored for non-linear patterns in composite images could contribute to even more substantial performance hikes. As MLLMs continue to evolve and incorporate richer datasets, the trajectory of their application and theoretical development appears promising.

In conclusion, this paper provides an insightful resource for the community, emphasizing the importance of diverse caption data in aligning visual and textual modalities, and suggesting a critical pathway towards more sophisticated multimodal understanding mechanisms. Such advancements pave the way for a range of practical applications across various industries that rely on the intricate interpretation of composite visual data.