Leveraging Foundation Model Adapters to Enable Robust and Semantic Underwater Exploration

Abstract

This position paper presents a framework for intelligent underwater exploration by marrying foundation models (FMs) with multi‑frequency echosounder data. Echosounder data capture backscattered acoustic signals across a range of frequencies, providing rich insights into underwater environments by exploiting the frequency‑dependent scattering properties of underwater targets. However, their heterogeneity and complex structure complicate analysis. To address these challenges, the paper introduces four key innovations aimed at improving echosounder data analysis under dynamic ocean conditions: (1) aligning multi‑frequency echosounder data with FMs via lightweight FM adapters, (2) enabling continual adaptation to temporal distribution shifts in dynamic marine environments, (3) designing semantic tokenizers that preserve spatial structures, and (4) effectively leveraging sparse annotations to minimize dependence on costly labeled data. For each research direction, we map recent artificial intelligence (AI) methodologies to marine acoustic challenges and outline concrete pathways for technology transfer. Preliminary experiments demonstrate that a Vision Transformer (ViT), pretrained on natural images in a self-supervised manner, can segment sandeel schools from multi‑frequency echosounder data without task‑specific retraining. These results substantiate the proposed framework and illustrate the potential of cross‑disciplinary AI methods for ecologically informative underwater exploration.