Vectra: 一种新的度量标准、数据集和模型用于电子商务图像中的图像翻译视觉质量评估
Vectra: A New Metric, Dataset, and Model for Visual Quality Assessment in E-Commerce In-Image Machine Translation
AI总结：
Vectra提出了一种无参考、基于大语言模型的视觉质量评估框架，通过多维度量系统、数据集和模型提升电子商务图像翻译的视觉质量评估效果。
摘要：
在图像中的机器翻译（IIMT）推动了跨境电子商务产品列表的生成；现有研究主要关注机器翻译评估，而视觉渲染质量对用户参与度至关重要。当面对密集的产品图像和多模态缺陷时，当前基于参考的方法（例如SSIM、FID）缺乏可解释性，而模型作为判断者的方法缺乏领域内细粒度的奖励信号。为弥补这一差距，我们引入Vectra，据我们所知，这是首个无参考、基于大语言模型（MLLM）的电子商务IIMT视觉质量评估框架。Vectra包含三个组成部分：（1）Vectra分数，一个多维质量度量系统，将视觉质量分解为14个可解释的维度，通过空间感知的缺陷面积比（DAR）量化来减少标注歧义；（2）Vectra数据集，通过多样性感知采样从110万张真实产品图像中构建，包含一个2K基准用于系统评估，3万条基于推理的注释用于指令微调，以及3500条专家标注的偏好用于对齐和评估；（3）Vectra模型，一个40亿参数的MLLM，能够生成定量分数和诊断推理。实验表明，Vectra在与人类排名的相关性上达到了最先进的水平，我们的模型在评分性能上优于领先的MLLM，包括GPT-5和Gemini-3。数据集和模型将在接受后发布。
英文摘要：
In-Image Machine Translation (IIMT) powers cross-border e-commerce product listings; existing research focuses on machine translation evaluation, while visual rendering quality is critical for user engagement. When facing context-dense product imagery and multimodal defects, current reference-based methods (e.g., SSIM, FID) lack explainability, while model-as-judge approaches lack domain-grounded, fine-grained reward signals. To bridge this gap, we introduce Vectra, to the best of our knowledge, the first reference-free, MLLM-driven visual quality assessment framework for e-commerce IIMT. Vectra comprises three components: (1) Vectra Score, a multidimensional quality metric system that decomposes visual quality into 14 interpretable dimensions, with spatially-aware Defect Area Ratio (DAR) quantification to reduce annotation ambiguity; (2) Vectra Dataset, constructed from 1.1M real-world product images via diversity-aware sampling, comprising a 2K benchmark for system evaluation, 30K reasoning-based annotations for instruction tuning, and 3.5K expert-labeled preferences for alignment and evaluation; and (3) Vectra Model, a 4B-parameter MLLM that generates both quantitative scores and diagnostic reasoning. Experiments demonstrate that Vectra achieves state-of-the-art correlation with human rankings, and our model outperforms leading MLLMs, including GPT-5 and Gemini-3, in scoring performance. The dataset and model will be released upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
