Inference-Path Optimization via Circuit Duplication in Frozen Visual Transformers for Marine Species Classification

ArXi:2604.03428v1 Announce Type: cross Automated underwater species classification is constrained by annotation cost and environmental variation that limits the transferability of fully supervised models. Recent work has shown that frozen embeddings from self-supervised vision foundation models already provide a strong label-efficient baseline for marine image classification. Here we investigate whether this frozen-embedding regime can be improved at inference time, without fine-tuning or changing model weights.