RetailKLIP：在单GPU上使用度量学习微调OpenCLIP骨干网络以实现零样本零售产品图像分类
RetailKLIP : Finetuning OpenCLIP backbone using metric learning on a single GPU for Zero-shot retail product image classification
AI总结：
本文提出RetailKLIP方法，通过度量学习在单GPU上微调OpenCLIP视觉编码器，使嵌入可直接用于最近邻分类，实现零样本零售产品图像分类，避免频繁重训练并节省资源。
摘要：
零售产品或包装杂货商品图像需要在各种计算机视觉应用中进行分类，例如自助结账商店、供应链自动化和零售执行评估。以往的工作探索了为此目的微调深度模型的方法。但由于微调大型模型甚至预训练骨干网络的线性层，都需要为分类范围内每新增一种零售产品运行至少几个周期的梯度下降，因此在现实场景中需要频繁重新训练。在这项工作中，我们提出以这样一种方式微调CLIP模型的视觉编码器：使其嵌入可以容易地用于基于最近邻的分类，同时获得接近或超过完全微调的准确率。基于最近邻的分类器对新产品的加入无需增量训练，从而节省资源和等待时间。
英文摘要：
Retail product or packaged grocery goods images need to classified in various computer vision applications like self checkout stores, supply chain automation and retail execution evaluation. Previous works explore ways to finetune deep models for this purpose. But because of the fact that finetuning a large model or even linear layer for a pretrained backbone requires to run at least a few epochs of gradient descent for every new retail product added in classification range, frequent retrainings are needed in a real world scenario. In this work, we propose finetuning the vision encoder of a CLIP model in a way that its embeddings can be easily used for nearest neighbor based classification, while also getting accuracy close to or exceeding full finetuning. A nearest neighbor based classifier needs no incremental training for new products, thus saving resources and wait time.
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。
