  Visual as well as genetic biometrics are routinely employed to identify
species and individuals in biological applications. However, no attempts have
been made in this domain to computationally enhance visual classification of
rare classes with little image data via genetics. In this paper, we thus
propose aligned visual-genetic inference spaces with the aim to implicitly
encode cross-domain associations for improved performance. We demonstrate for
the first time that such alignment can be achieved via deep embedding models
and that the approach is directly applicable to boosting long-tailed
recognition (LTR) particularly for rare species. We experimentally demonstrate
the efficacy of the concept via application to microscopic imagery of 30k+
planktic foraminifer shells across 32 species when used together with
independent genetic data samples. Most importantly for practitioners, we show
that visual-genetic alignment can significantly benefit visual-only recognition
of the rarest species. Technically, we pre-train a visual ResNet50 deep
learning model using triplet loss formulations to create an initial embedding
space. We re-structure this space based on genetic anchors embedded via a
Sequence Graph Transform (SGT) and linked to visual data by cross-domain cosine
alignment. We show that an LTR approach improves the state-of-the-art across
all benchmarks and that adding our visual-genetic alignment improves per-class
and particularly rare tail class benchmarks significantly further. We conclude
that visual-genetic alignment can be a highly effective tool for complementing
visual biological data containing rare classes. The concept proposed may serve
as an important future tool for integrating genetics and imageomics towards a
more complete scientific representation of taxonomic spaces and life itself.
Code, weights, and data splits are published for full reproducibility.
