What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits
<|reference_start|>What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits: A grand challenge in biology is to discover evolutionary traits - features of organisms common to a group of species with a shared ancestor in the tree of life (also referred to as phylogenetic tree). With the growing availability of image repositories in biology, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototypes. However, current prototype-based methods are mostly designed to operate over a flat structure of classes and face several challenges in discovering hierarchical prototypes, including the issue of learning over-specific features at internal nodes. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net). We empirically show that HComP-Net learns prototypes that are accurate, semantically consistent, and generalizable to unseen species in comparison to baselines on birds, butterflies, and fishes datasets. The code and datasets are available at https://github.com/Imageomics/HComPNet.<|reference_end|>
@article{manogaran2024what,
    title={What Do You See in Common? Learning Hierarchical Prototypes over
  Tree-of-Life to Discover Evolutionary Traits},
    author={Harish Babu Manogaran, M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Caleb
  Patrick Charpentier, Josef C. Uyeda, Wasila Dahdul, Matthew J Thompson,
  Elizabeth G Campolongo, Kaiya L Provost, Paula M. Mabee, Hilmar Lapp, Anuj
  Karpatne},
    journal={arXiv preprint arXiv:2409.02335},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02335},
    primaryClass={cs.CV}
}