Rove-Tree-11: The Not-so-Wild Rover a Hierarchically Structured Image Dataset for Deep Metric Learning Research
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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We present a new dataset of images of pinned insects from museum collections along with a ground truth phylogeny (a graph representing the relative evolutionary distance between species). The images include segmentations, and can be used for clustering and deep hierarchical metric learning. As far as we know, this is the first dataset released specifically for generating phylogenetic trees. We provide several benchmarks for deep metric learning using a selection of state-of-the-art methods.
|Computer Vision – ACCV 2022 - 16th Asian Conference on Computer Vision, Proceedings
|Lei Wang, Juergen Gall, Tat-Jun Chin, Imari Sato, Rama Chellappa
|Udgivet - 2023
|16th Asian Conference on Computer Vision, ACCV 2022 - Macao, Kina
Varighed: 4 dec. 2022 → 8 dec. 2022
|16th Asian Conference on Computer Vision, ACCV 2022
|04/12/2022 → 08/12/2022
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Many people have been involved in this project. First we would like to thank David Gutschenreiter, Søren Bech and André Fastrup who took the photos of the unit trays and completed the initial segmentations of the images as part of their theses. Next, we would like to thank Alexey Solodovnikov of the Natural History Museum of Denmark for providing the specimens, the ground truth phylogeny and guidance for all things entomological. Also, thanks Francois Lauze and the entire Phylorama team for their input the project.
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.