基于弧面损失与扩散重排的食品检索方法，获竞赛第三名
3rd Place Solution to ICCV LargeFineFoodAI Retrieval
- 采用加权弧面损失与圆损失训练四模型，提升特征表达能力
- 通过测试时增强与集成策略，使公开/私有榜单mAP@100达0.81219和0.81191
- 提出基于扩散与k互惠重排的新重排方法，适用于高精度图像检索
本文介绍在Kaggle平台ICCV LargeFineFoodAI Retrieval竞赛中获得第三名的解决方案。四个基础模型分别使用弧面损失与圆损失的加权和进行独立训练，随后依次应用测试时增强（TTA）与模型集成（Ensemble）以提升特征表示能力。此外，提出一种基于扩散模型与k互惠重排的新型重排方法用于检索优化。最终，该方法在公开和私有排行榜上的mAP@100得分分别为0.81219和0.81191。
原文摘要 · Abstract (English)
This paper introduces the 3rd place solution to the ICCV LargeFineFoodAI Retrieval Competition on Kaggle. Four basic models are independently trained with the weighted sum of ArcFace and Circle loss, then TTA and Ensemble are successively applied to improve feature representation ability. In addition, a new reranking method for retrieval is proposed based on diffusion and k-reciprocal reranking. Finally, our method scored 0.81219 and 0.81191 mAP@100 on the public and private leaderboard, respectively.
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