# GHiML

Hierarchical multi-label classification of seafloor imagery has relied on low-level visual features such as color, texture and shape, leaving rare classes and shifting illumination hard to resolve. GHiML extracts concept variables tied to the training data from a vision language model, generates per-sample captions grounded in them, and fuses the resulting text embeddings with image embeddings through cross-attention and a per-node gated router. Across BenthicNet, FathomNet and iNaturalist, the router improves macro F1 over the vision-only baseline by up to 6.7 percentage points, and by up to 21.1 points on in-domain benthic data, with gains persisting under distribution shift.

Hierarchical multi-label classification of seafloor imagery has relied on low-level visual features such as color, texture and shape, leaving rare classes and shifting illumination hard to resolve. GHiML extracts concept variables tied to the training data from a vision language model, generates per-sample captions grounded in them, and fuses the resulting text embeddings with image embeddings through cross-attention and a per-node gated router. Across BenthicNet, FathomNet and iNaturalist, the router improves macro F1 over the vision-only baseline by up to 6.7 percentage points, and by up to 21.1 points on in-domain benthic data, with gains persisting under distribution shift.