Detecting visually similar images is a particularly useful attribute to look
to when calculating product recommendations. Embedding similarity, which
utilizes pre-trained computer vision models to extract high-level image
features, has demonstrated remarkable efficacy in identifying images with
similar compositions. However, there is a lack of methods for evaluating the
embeddings generated by these models, as conventional loss and performance
metrics do not adequately capture their performance in image similarity search
tasks.
In this paper, we evaluate the viability of the image embeddings from
numerous pre-trained computer vision models using a novel approach named
CorrEmbed. Our approach computes the correlation between distances in image
embeddings and distances in human-generated tag vectors. We extensively
evaluate numerous pre-trained Torchvision models using this metric, revealing
an intuitive relationship of linear scaling between ImageNet1k accuracy scores
and tag-correlation scores. Importantly, our method also identifies deviations
from this pattern, providing insights into how different models capture
high-level image features.
By offering a robust performance evaluation of these pre-trained models,
CorrEmbed serves as a valuable tool for researchers and practitioners seeking
to develop effective, data-driven approaches to similar item recommendations in
fashion retail.

利用预训练的计算机视觉模型提取高级图像特征的图像嵌入相似性在识别具有相似组成的图像方面表现出显著的效果。本文提出了一种名为 CorrEmbed 的新方法，通过计算图像嵌入和人工生成的标签向量之间的距离相关性来评估来自多个预训练的计算机视觉模型的图像嵌入的可行性。我们使用这个指标广泛评估了多种预训练的 Torchvision 模型，揭示了 ImageNet1k 准确率分数和标签相关性分数之间的线性关系。值得注意的是，我们的方法还可以识别出与这一模式偏离的情况，从而揭示了不同模型如何捕捉高级图像特征。CorrEmbed 为研究人员和实践者在时尚零售中开发有效的基于数据驱动的相似物品推荐方法提供了一个强大的性能评估工具。