Good Scores, Bad Data：多模态一致性评分 MCS
Good Scores, Bad Data: A Metric for Multimodal Coherence
AI 导读
一篇 arXiv 论文提出多模态一致性评分 MCS，用于在不依赖任何下游模型的情况下评估多模态融合质量，将一致性分解为身份、空间、语义、决策四个维度。
正文 · 原文
Abstract:Multimodal AI systems are evaluated by downstream task accuracy, but high accuracy does not mean the underlying data is coherent. A model can score well on Visual Question Answering (VQA) while its inputs contradict each other. We introduce the Multimodal Coherence Score (MCS), a metric that evaluates fusion quality independent of any downstream model. MCS decomposes coherence into four dimensions, identity, spatial, semantic, and decision, with weights learned via Nelder-Mead optimization. We evaluate on 1,000 Visual Genome images using DETR, CLIP, and ViLT, and validate on 150 COCO images with no retraining. Across three fusion architectures, MCS discriminates quality with higher sensitivity than task accuracy alone (Spearman rho = 0.093 vs. 0.071). Perturbation experiments confirm each dimension responds independently to its failure mode with zero cross-talk. MCS is lightweight, requires no human annotation, and tells you not just that something broke, but what broke.
| Comments: | 9 pages, 6 figures, NeurIPS 2024 format |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2603.25924 [cs.CV] |
| (or arXiv:2603.25924v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.25924 arXiv-issued DOI via DataCite |
Submission history
From: Vasundra Srinivasan [view email]
[v1]
Thu, 26 Mar 2026 21:30:34 UTC (2,259 KB)
[v2]
Sat, 24 Oct 2026 00:43:12 UTC (2,259 KB)
来源：arXiv cs.AI · arxiv.org