我们真正测量的是什么？通过无监督语义聚类重新思考网页级自然图像集合中的数据集偏差
What Are We Really Measuring? Rethinking Dataset Bias in Web-Scale Natural Image Collections via Unsupervised Semantic Clustering
AI总结：
本文通过无监督语义聚类重新评估数据集偏差，揭示传统监督分类方法高估语义偏差的问题。
摘要：
在计算机视觉中，量化数据集偏差的常见方法是训练模型区分不同数据集。高分类准确率被解释为有意义的语义差异证据。该方法假设标准图像增强能抑制低级非语义线索，任何剩余性能必须反映真实的语义差异。本文证明在大规模自然图像集合领域，这一基本假设是错误的。高分类准确率常由基于分辨率的伪影驱动，这些伪影源于原生图像分辨率分布和缩放过程中的插值效应。这些伪影形成稳健的、数据集特定的特征，即使在传统图像损坏下仍存在。通过受控实验，我们显示模型即使在非语义、程序生成的图像上也能实现强数据集分类，证明其依赖于表面线索。为解决此问题，我们重新审视这一几十年的老想法数据集分离性，但不通过监督分类。相反，我们引入一种无监督方法来测量真实的语义分离性。我们的框架通过聚类语义丰富的基础视觉模型特征直接评估语义相似性，有意绕过对数据集标签的监督分类。当应用于主要网页级数据集时，该工作主要关注点，传统监督方法报告的高分离性大多消失，聚类准确率降至接近偶然水平。这揭示了传统基于分类的评估系统性地高估语义偏差的幅度非常大。
英文摘要：
In computer vision, a prevailing method for quantifying dataset bias is to train a model to distinguish between datasets. High classification accuracy is then interpreted as evidence of meaningful semantic differences. This approach assumes that standard image augmentations successfully suppress low-level, non-semantic cues, and that any remaining performance must therefore reflect true semantic divergence. We demonstrate that this fundamental assumption is flawed within the domain of large-scale natural image collections. High classification accuracy is often driven by resolution-based artifacts, which are structural fingerprints arising from native image resolution distributions and interpolation effects during resizing. These artifacts form robust, dataset-specific signatures that persist despite conventional image corruptions. Through controlled experiments, we show that models achieve strong dataset classification even on non-semantic, procedurally generated images, proving their reliance on superficial cues. To address this issue, we revisit this decades-old idea of dataset separability, but not with supervised classification. Instead, we introduce an unsupervised approach that measures true semantic separability. Our framework directly assesses semantic similarity by clustering semantically-rich features from foundational vision models, deliberately bypassing supervised classification on dataset labels. When applied to major web-scale datasets, the primary focus of this work, the high separability reported by supervised methods largely vanishes, with clustering accuracy dropping to near-chance levels. This reveals that conventional classification-based evaluation systematically overstates semantic bias by an overwhelming margin.
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。
