mlsanityData to 14 Oct 2026

3rd Place Solution to ICCV LargeFineFoodAI Retrieval

arXiv 2510.21198 ↗First posted 24 Oct 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 24 Oct 2025.

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References · 7

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  3. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  4. 2020ResNeSt: Split-Attention Networkscited by 445
  5. 2020Circle Loss: A Unified Perspective of Pair Similarity Optimizationcited by 403
  6. 2017Re-ranking Person Re-identification with k-reciprocal Encodingcited by 262
  7. 2018Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processingcited by 20

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