CompCap: Improving Multimodal Large Language Models with Composite
  Captions
Authors: name: Xiaohui Chen, name: Satya Narayan Shukla, name: Mahmoud Azab, name: Aashu Singh, name: Qifan Wang, name: David Yang, name: ShengYun Peng, name: Hanchao Yu, name: Shen Yan, name: Xuewen Zhang, name: Baosheng He

How well can Multimodal Large Language Models (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 Large Language Models (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.