FGVC7研讨会上的半监督iNaturalist-Aves挑战赛
The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop
摘要：
本文档描述了我们在CVPR 2020的FGVC 7研讨会上为半监督识别挑战~ \cite{semi-aves}收集的新数据集背后的细节和动机。该数据集包含从iNat-2018数据集中采样的1000种鸟类，总共近15万张图像。从这个集合中，我们对类的子集及其标签进行采样，同时将剩余类中的图像添加到未标记的图像集中。域外数据（新类），高类不平衡，类之间的细粒度相似性的存在对现有的半监督识别技术在文献中提出了重大挑战。数据集可在此处获得：\url{https://github.com/cvl-umass/semi-inat-2020}
英文摘要：
This document describes the details and the motivation behind a new dataset we collected for the semi-supervised recognition challenge~\cite{semi-aves} at the FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds sampled from the iNat-2018 dataset for a total of nearly 150k images. From this collection, we sample a subset of classes and their labels, while adding the images from the remaining classes to the unlabeled set of images. The presence of out-of-domain data (novel classes), high class-imbalance, and fine-grained similarity between classes poses significant challenges for existing semi-supervised recognition techniques in the literature. The dataset is available here: \url{https://github.com/cvl-umass/semi-inat-2020}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
