12 papers
Self-Distillation of Hidden Layers for Self-Supervised Representation Learning
Scott C. Lowe, Anthony Fuller, Sageev Oore +2
The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.g. MAE) that reconstruct raw low-level data, and predictive approaches (e.g. I-JE…
A continental-scale dataset of ground beetles with high-resolution images and validated morphological trait measurements
S M Rayeed, Mridul Khurana, Alyson East +18
Despite the ecological significance of invertebrates, global trait databases remain heavily biased toward vertebrates and plants, limiting comprehensive ecological analyses of high…
BarcodeMamba+: Advancing State-Space Models for Fungal Biodiversity Research
Tiancheng Gao, Scott C. Lowe, Brendan Furneaux +2
Accurate taxonomic classification from DNA barcodes is a cornerstone of global biodiversity monitoring, yet fungi present extreme challenges due to sparse labelling and long-tailed…
CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale
ZeMing Gong, Austin T. Wang, Xiaoliang Huo +4
Measuring biodiversity is crucial for understanding ecosystem health. While prior works have developed machine learning models for taxonomic classification of photographic images a…
Hyperbolic Multimodal Representation Learning for Biological Taxonomies
ZeMing Gong, Chuanqi Tang, Xiaoliang Huo +6
Taxonomic classification in biodiversity research involves organizing biological specimens into structured hierarchies based on evidence, which can come from multiple modalities su…
Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens
Alyson East, Elizabeth G. Campolongo, Luke Meyers +25
1) Biological collections house millions of specimens with digital images increasingly available through open-access platforms. However, most imaging protocols were developed for h…