2nd Place Solution to Google Universal Image Embedding
This is an incremental solution for a specific competition in computer vision, aimed at improving image embedding performance.
The paper tackled the Google Universal Image Embedding Competition by using an instance-level fine-grained image classification method, focusing on data building, model structure, and training strategies, resulting in scores of 0.713 on the public leaderboard and 0.709 on the private leaderboard.
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.