Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
Multimodal large language models (MLLMs) excel at generating highly detailed
captions but often produce hallucinations. Our analysis reveals that existing
hallucination detection methods struggle with detailed captions. We attribute
this to the increasing reliance of MLLMs on their generated text, rather than
the input image, as the sequence length grows. To address this issue, we
propose a multiagent approach that leverages LLM-MLLM collaboration to correct
given captions. Additionally, we introduce an evaluation framework and a
benchmark dataset to facilitate the systematic analysis of detailed captions.
Our experiments demonstrate that our proposed evaluation method better aligns
with human judgments of factuality than existing metrics and that existing
approaches to improve the MLLM factuality may fall short in hyper-detailed
image captioning tasks. In contrast, our proposed method significantly enhances
the factual accuracy of captions, even improving those generated by GPT-4V.
Finally, we highlight a limitation of VQA-centric benchmarking by demonstrating
that an MLLM's performance on VQA benchmarks may not correlate with its ability
to generate detailed image captions.
