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.

翻译：视觉与遗传生物特征识别在生物学应用中常被用于物种及个体鉴定。然而，在该领域，尚未有研究尝试通过遗传信息计算性地增强图像数据稀少的罕见类别的视觉分类能力。为此，本文提出对齐的视觉-遗传推理空间，旨在隐式编码跨领域关联以提升分类性能。我们首次证明，此类对齐可通过深度嵌入模型实现，且该方法可直接应用于长尾识别（LTR）增强，尤其针对珍稀物种。通过涵盖32个物种、3万余枚浮游有孔虫壳体的显微图像数据，结合独立遗传样本，我们实验验证了该概念的有效性。对实践者最为重要的是，我们发现视觉-遗传对齐可显著提升仅依赖视觉识别的珍稀物种分类性能。技术层面，我们采用三元组损失预训练视觉ResNet50深度学习模型，构建初始嵌入空间；通过序列图变换（SGT）嵌入遗传锚点，并利用跨领域余弦对齐将其与视觉数据关联，重构该嵌入空间。实验表明，LTR方法在所有基准测试中均达到最优，而加入视觉-遗传对齐后，逐类别尤其是稀有尾类别的指标获得显著提升。由此得出结论：视觉-遗传对齐可作为补充包含罕见类别的视觉生物数据的高效工具。这一概念有望成为未来整合遗传学与图像组学的重要方法，推动分类学空间乃至生命表征的科学完整性。为保障完全可复现性，本文公开了代码、模型权重及数据划分。