3rd Place Solution to ICCV LargeFineFoodAI Retrieval
This work addresses food image retrieval for competition participants, presenting an incremental improvement with a new reranking approach.
The paper tackled the ICCV LargeFineFoodAI Retrieval Competition by training four models with combined ArcFace and Circle loss, applying TTA and ensemble techniques, and proposing a diffusion and k-reciprocal reranking method, achieving mAP@100 scores of 0.81219 and 0.81191 on public and private leaderboards.
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.