What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits
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arXiv
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| Natura: | Preprint |
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2024
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| author | Manogaran, Harish Babu Maruf, M. Daw, Arka Mehrab, Kazi Sajeed Charpentier, Caleb Patrick Uyeda, Josef C. Dahdul, Wasila Thompson, Matthew J Campolongo, Elizabeth G Provost, Kaiya L Chao, Wei-Lun Berger-Wolf, Tanya Mabee, Paula M. Lapp, Hilmar Karpatne, Anuj |
| author_facet | Manogaran, Harish Babu Maruf, M. Daw, Arka Mehrab, Kazi Sajeed Charpentier, Caleb Patrick Uyeda, Josef C. Dahdul, Wasila Thompson, Matthew J Campolongo, Elizabeth G Provost, Kaiya L Chao, Wei-Lun Berger-Wolf, Tanya Mabee, Paula M. Lapp, Hilmar Karpatne, Anuj |
| contents | 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 prototypes at internal nodes. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net). The key novelties in HComP-Net include a novel over-specificity loss to avoid learning over-specific prototypes, a novel discriminative loss to ensure prototypes at an internal node are absent in the contrasting set of species with different ancestry, and a novel masking module to allow for the exclusion of over-specific prototypes at higher levels of the tree without hampering classification performance. We empirically show that HComP-Net learns prototypes that are accurate, semantically consistent, and generalizable to unseen species in comparison to baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02335 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits Manogaran, Harish Babu Maruf, M. Daw, Arka Mehrab, Kazi Sajeed Charpentier, Caleb Patrick Uyeda, Josef C. Dahdul, Wasila Thompson, Matthew J Campolongo, Elizabeth G Provost, Kaiya L Chao, Wei-Lun Berger-Wolf, Tanya Mabee, Paula M. Lapp, Hilmar Karpatne, Anuj Computer Vision and Pattern Recognition 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 prototypes at internal nodes. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net). The key novelties in HComP-Net include a novel over-specificity loss to avoid learning over-specific prototypes, a novel discriminative loss to ensure prototypes at an internal node are absent in the contrasting set of species with different ancestry, and a novel masking module to allow for the exclusion of over-specific prototypes at higher levels of the tree without hampering classification performance. We empirically show that HComP-Net learns prototypes that are accurate, semantically consistent, and generalizable to unseen species in comparison to baselines. |
| title | What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.02335 |