[2604.01619] Automatic Image-Level Morphological Trait Annotation for Organismal Images

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.01619 (cs) [Submitted on 2 Apr 2026 (v1), last revised 7 Apr 2026 (this version, v2)] Title:Automatic Image-Level Morphological Trait Annotation for Organismal Images Authors:Vardaan Pahuja, Samuel Stevens, Alyson East, Sydne Record, Yu Su View a PDF of the paper titled Automatic Image-Level Morphological Trait Annotation for Organismal Images, by Vardaan Pahuja and 4 other authors View PDF HTML (experimental) Abstract: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-language prompting to generate interpretable trait descriptions. Using this approach, we construct Bioscan-Traits, a dataset of 80K trait annotations spanning 19K insect images from BIOSCAN-5M. Human evaluation confirms the biological plausibility of the generated morphological descriptions. We assess desi...