Knowledge-Augmented Vision Language Models for Underwater Bioacoustic Spectrogram Analysis
量子物理
2026-03-10 v3
摘要
Marine mammal vocalization analysis depends on interpreting bioacoustic spectrograms. Vision Language Models (VLMs) are not trained on these domain-specific visualizations. We investigate whether VLMs can extract meaningful patterns from spectrograms visually. Our framework integrates VLM interpretation with LLM-based validation to build domain knowledge. This enables adaptation to acoustic data without manual annotation or model retraining.
引用
@article{arxiv.2509.05704,
title = {Tunneling of bosonic qubits under local dephasing through microscopic approach},
author = {Alberto Ferrara and Farzam Nosrati and Andrea Smirne and Jyrki Piilo and Rosario Lo Franco},
journal= {arXiv preprint arXiv:2509.05704},
year = {2026}
}
备注
20 pages, 13 figures