Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning

Qinghao Ye, Xianhan Zeng, Fu Li, Chunyuan Li, Haoqi Fan

Keywords: Vision-Language Model, Detailed Image Captioning, Caption Metric, Alignment, Preference Optimization, Large Language Models

TL;DR: We introduce DeCapBench and a novel evaluation metric DCScore for detailed image captioning evaluation. While with a fine-grained feedback collection method enabling the model to achieve superior performance across various tasks.

Abstract: Image captioning has long been a pivotal task in visual understanding, with recent advancements in vision-language models (VLMs) significantly enhancing the ability to generate detailed image captions. However, the evaluation of detailed image captioning remains underexplored due to outdated evaluation metrics and coarse annotations. In this paper, we introduce DeCapBench along with a novel metric, DCScore, specifically designed for detailed captioning tasks. DCScore evaluates hallucinations and fine-grained comprehensiveness by deconstructing responses into the smallest self-sufficient units, termed primitive information units, and assessing them individually. Our evaluation shows that DCScore aligns more closely with human judgment than other rule-based or model-based metrics. Concurrently, DeCapBench exhibits a high correlation with VLM arena results on descriptive tasks, surpassing existing benchmarks for vision-language models. Additionally, we present an automatic fine-grained feedback collection method, FeedQuill, for preference optimization based on our advanced metric, demonstrating robust generalization capabilities across auto-generated preference data. Extensive experiments on multiple VLMs demonstrate that our method not only significantly reduces hallucinations but also enhances performance across various benchmarks, achieving superior detail captioning performance while surpassing GPT-4o.

Primary Area: datasets and benchmarks

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Submission Number: 2960