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arXiv · 2509.16538

VC-Inspector: Advancing Reference-free Evaluation of Video Captions with Factual Analysis

Shubhashis Roy Dipta·Tz-Ying Wu·Subarna Tripathi
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Abstract

We propose VC-Inspector, a lightweight, open-source large multimodal model (LMM) for reference-free evaluation of video captions, with a focus on factual accuracy. Unlike existing metrics that suffer from limited context handling, weak factuality assessment, or reliance on proprietary services, VC-Inspector offers a reproducible and fact-aware alternative that aligns closely with human judgments. To enable robust training and interpretable evaluation, we introduce a systematic framework for generating captions with controllable factual errors, paired with graded quality scores and explanatory annotations. Experiments demonstrate that VC-Inspector achieves state-of-the-art correlation with human judgments, generalizing across diverse domains (e.g., VATEX-Eval, Flickr8K-Expert, and Flickr8K-CF benchmarks) and revealing the potential for caption improvement. Project page is available at https://dipta007.github.io/VC-Inspector

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Shubhashis Roy Dipta, Tz-Ying Wu, Subarna Tripathi. 2026-04-19. VC-Inspector: Advancing Reference-free Evaluation of Video Captions with Factual Analysis. https://arxiv.org/abs/2509.16538

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