Label-efficient underwater species classification with semi-supervised learning on frozen foundation model embeddings

ArXi:2604.00313v1 Announce Type: new Automated species classification from underwater imagery is bottlenecked by the cost of expert annotation, and supervised models trained on one dataset rarely transfer to new conditions. We investigate whether semi-supervised methods operating on frozen foundation model embeddings can close this annotation gap with minimal labeling effort. Using DINOv3 ViT-B embeddings with no fine-tuning, we propagate a small set of labeled seeds through unlabeled data via nearest-neighbor-based self.