Title: Fine-Grained Image Retrieval with Neighbor-Attention Label Correction. Abstract: This paper studies noise-resistant deep model training for the fine-grained image retrieval task, which has a unconstrained target label space and suffers from the difficulty of acquiring accurate fine-grained labels. A Neighbor-Attention Label Correction (NALC) model is proposed based on the meta-learning framework to correct labels during the training stage. A training batch and a validation batch are sampled from the training set, which hence allows to optimize the NALC model by referring to the validation batch. We also propose a novel nested optimization for the meta-learning framework to enhance the optimization efficiency. The training procedure consistently boosts the label accuracy in training batch, which in-turn ensures a more accurate training set. Experiments results show that our method boosts the label accuracy from 70% to 97+% and it outperforms recent works up to 11.5% in rank1 accuracy on various fine-grained image retrieval tasks, e.g., fine-grained instance retrieval on CUB200 and CARS, as well as person re-identification, respectively. Ablation studies also show the NALC generalizes well on different types of noises, e.g., Asymmetric, Pair-Flip, Pattern noises, etc.