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InfoTaxa: Information-Calibrated Label-Free Clustering for Fine-Grained Visual Taxonomy

Paper recorded by Signals 4 on 2026-09-15 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16

Category: cs.CV · 计算机视觉 · first seen 2026-09-16

Abstract

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

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#41 most recent of 237 cs.CV papers we have recorded · ↑ newer: FROD: Feature Matching Residual Denoising Oracle Bone Decipher · ↓ older: Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly
Cite this page: InfoTaxa: Information-Calibrated Label-Free Clustering for Fine-Grained Visual Taxonomy: the #41 most recent of 237 cs.CV papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/infotaxa-information-calibrated-label-free-clustering-for-fine-grained-visual-ta.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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