Current perceptual similarity metrics operate at the level of pixels and patches. These metrics compare images in terms of their low-level colors and textures, but fail to capture mid-level similarities and differences in image layout, object pose, and semantic content. In this paper, we develop a perceptual metric that assesses images holistically. Our first step is to collect a new dataset of human similarity judgments over image pairs that are alike in diverse ways. Critical to this dataset is that judgments are nearly automatic and shared by all observers. To achieve this we use recent text-to-image models to create synthetic pairs that are perturbed along various dimensions. We observe that popular perceptual metrics fall short of explaining our new data, and we introduce a new metric, DreamSim, tuned to better align with human perception. We analyze how our metric is affected by different visual attributes, and find that it focuses heavily on foreground objects and semantic content while also being sensitive to color and layout. Notably, despite being trained on synthetic data, our metric generalizes to real images, giving strong results on retrieval and reconstruction tasks. Furthermore, our metric outperforms both prior learned metrics and recent large vision models on these tasks.

翻译：当前的感知相似性度量标准主要基于像素和图像块层面。这些度量通过低级颜色和纹理特征比较图像，但未能捕捉图像布局、物体姿态和语义内容等中级相似性与差异。本文开发了一种整体评估图像的感知度量。首要步骤是收集一个关于图像对的人类相似性判断新数据集，这些图像对以多种方式相似。该数据集的关键在于判断近乎自动且被所有观察者共享。为此，我们利用近期文本到图像模型生成沿不同维度扰动的合成图像对。我们观察到现有的感知度量无法充分解释新数据，因此引入新度量DreamSim，使其与人类感知更契合。我们分析了不同视觉属性对该度量的影响，发现它高度关注前景物体和语义内容，同时对颜色和布局敏感。值得注意的是，尽管基于合成数据训练，该度量仍能泛化至真实图像，在检索与重建任务上表现优异。此外，在这些任务上，我们的度量优于先前的学习度量以及近期大型视觉模型。