The notion of visual similarity is essential for computer vision, and in
applications and studies revolving around vector embeddings of images. However,
the scarcity of benchmark datasets poses a significant hurdle in exploring how
these models perceive similarity. Here we introduce Style Aligned Artwork
Datasets (SALADs), and an example of fruit-SALAD with 10,000 images of fruit
depictions. This combined semantic category and style benchmark comprises 100
instances each of 10 easy-to-recognize fruit categories, across 10 easy
distinguishable styles. Leveraging a systematic pipeline of generative image
synthesis, this visually diverse yet balanced benchmark demonstrates salient
differences in semantic category and style similarity weights across various
computational models, including machine learning models, feature extraction
algorithms, and complexity measures, as well as conceptual models for
reference. This meticulously designed dataset offers a controlled and balanced
platform for the comparative analysis of similarity perception. The SALAD
framework allows the comparison of how these models perform semantic category
and style recognition task to go beyond the level of anecdotal knowledge,
making it robustly quantifiable and qualitatively interpretable.

通过介绍一种包含 10000 个水果图像的样式对齐艺术数据集（SALADs），对计算模型在语义类别和样式相似性感知方面的表现进行了定量和质量可解释的比较分析。