Automatic Image-Level Morphological Trait Annotation for Organismal Images
Morphological traits are physical characteristics of biological organisms that provide vital clues on how organisms interact with their environment. Yet extracting these traits remains a slow, expert-driven process, limiting their use in large-scale ecological studies. A major bottleneck is the absence of high-quality datasets linking biological images to trait-level annotations. In this work, we demonstrate that sparse autoencoders trained on foundation-model features yield monosemantic, spatially grounded neurons that consistently activate on meaningful morphological parts. Leveraging this property, we introduce a trait annotation pipeline that localizes salient regions and uses vision-...
| Authors | Vardaan Pahuja, Samuel Stevens, Alyson East et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 8 May 2026 |
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