Product retrieval is of great importance in the ecommerce domain. This paper
introduces our 1st-place solution in eBay eProduct Visual Search Challenge
(FGVC9), which is featured for an ensemble of about 20 models from vision
models and vision-language models. While model ensemble is common, we show that
combining the vision models and vision-language models brings particular
benefits from their complementarity and is a key factor to our superiority.
Specifically, for the vision models, we use a two-stage training pipeline which
first learns from the coarse labels provided in the training set and then
conducts fine-grained self-supervised training, yielding a coarse-to-fine
metric learning manner. For the vision-language models, we use the textual
description of the training image as the supervision signals for fine-tuning
the image-encoder (feature extractor). With these designs, our solution
achieves 0.7623 MAR@10, ranking the first place among all the competitors. The
code is available at: \href{this https URL}{V$^2$L}.

本文介绍了在 eBay 产品视觉搜索挑战 (FGVC9) 中获得第一名的一个模型。该模型通过将视觉模型和视觉语言模型相结合，运用 20 个模型的结合方式，在对 coarse labels 进行两阶段训练的基础上，进行了精细化的自我监督训练。此外，该模型通过使用文本描述训练图像作为监督信号，对图像编码器进行了微调。最终，该模型达到了 0.7623 MAR@10 的成绩，超过了所有竞争对手。