Image representations are a critical building block of computer vision
applications. This paper presents the 2nd place solution to the Google
Universal Image Embedding Competition, which is part of the ECCV2022
instance-level recognition workshops. We use the instance-level fine-grained
image classification method to complete this competition. We focus on data
building and processing, model structure, and training strategies. Finally, the
solution scored 0.713 on the public leaderboard and 0.709 on the private
leaderboard.

本文介绍了第二名在 Google 通用图像嵌入竞赛中的解决方案，主要探讨了数据建立，模型结构和训练策略对细粒度图像分类的影响，并在公共排行榜得分为 0.713，在私人排行榜得分为 0.709。

Google 通用图像嵌入竞赛第二名解决方案

2nd Place Solution to Google Universal Image Embedding

Data building for automatic post-editing (APE) requires extensive and
expert-level human effort, as it contains an elaborate process that involves
identifying errors in sentences and providing suitable revisions. Hence, we
develop a self-supervised data generation tool, deployable as a web
application, that minimizes human supervision and constructs personalized APE
data from a parallel corpus for several language pairs with English as the
target language. Data-centric APE research can be conducted using this tool,
involving many language pairs that have not been studied thus far owing to the
lack of suitable data.

开发了自我监督的数据生成工具，可以用于多种语言对的自动后编辑（APE）数据建设，以支持基于数据的 APE 研究，解决目前由于数据不足而无法进行的语言对研究问题。