{"ID":2850503,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.21198","arxiv_id":"2510.21198","title":"3rd Place Solution to ICCV LargeFineFoodAI Retrieval","abstract":"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.","short_abstract":"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 m...","url_abs":"https://arxiv.org/abs/2510.21198","url_pdf":"https://arxiv.org/pdf/2510.21198v1","authors":"[\"Yang Zhong\",\"Zhiming Wang\",\"Zhaoyang Li\",\"Jinyu Ma\",\"Xiang Li\"]","published":"2025-10-24T07:04:32Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}