Knowledge-Augmented Vision Language Models for Underwater Bioacoustic Spectrogram Analysis
Ragib Amin Nihal, Benjamin Yen, Takeshi Ashizawa, Kazuhiro Nakadai
9/6/2025
cs.CVcs.AIcs.IR
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Abstract
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.
