Related papers: The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop

The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop

URL: http://arxiv.org/abs/2103.06937v1
Date: Thu, 11 Mar 2021 20:21:16 GMT
Title: The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop
Authors: Jong-Chyi Su and Subhransu Maji
Abstract summary: This document describes the details and the motivation behind a new dataset we collected for the semi-supervised recognition challengecitesemi-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.
Score: 42.02670649470055
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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}

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