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.
@article{arxiv.2510.21198,
title = {3rd Place Solution to ICCV LargeFineFoodAI Retrieval},
author = {Yang Zhong and Zhiming Wang and Zhaoyang Li and Jinyu Ma and Xiang Li},
journal= {arXiv preprint arXiv:2510.21198},
year = {2025}
}