SOURCE-LINKED INTELLIGENCE
InfoTaxa: Information-Calibrated Label-Free Clustering for Fine-Grained Visual Taxonomy
Label-free clustering of frozen pretrained visual embeddings offers a scalable route to biodiversity monitoring, but image-only fine-grained taxonomy exhibits a consistent coarse-to-fine failure mode: clusters recover broad taxonomic structure yet plateau at species level. We study this behaviour on BIOSCAN-5M through an information-calibrated clustering analysis. BioCLIP~2 features with UMAP and HDBSCAN reach $0.79$ AMI at family and $0.67$ at genus, substantially improving over the prior image baseline and remaining competitive with oracle-$K$, graph-based, and learned clustering heads on th
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T14:03:52.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.