论文标题
在嘈杂且细粒度的数据集中的半监督识别
Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
论文作者
论文摘要
Simi监督识别挑战-FGVC7是一项具有挑战性的细粒度识别竞赛。这项竞赛的困难之一是如何使用未标记的数据。我们采用了伪标签数据挖掘，以增加培训数据的量。另一个是如何识别出很小差异的类似鸟，尤其是在实例中具有相对微小的主体的鸟类。我们结合了通用图像识别和细粒图像识别方法来解决问题。所有通用图像识别模型均使用Paddleclas训练。利用两种不同的深度识别模型的结合，我们终于赢得了比赛的第三名。
Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adopted pseudo-tag data mining to increase the amount of training data. The other one is how to identify similar birds with a very small difference, especially those have a relatively tiny main-body in examples. We combined generic image recognition and fine-grained image recognition method to solve the problem. All generic image recognition models were training using PaddleClas . Using the combination of two different ways of deep recognition models, we finally won the third place in the competition.
