{"ID":2873967,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05703","arxiv_id":"2509.05703","title":"Knowledge-Augmented Vision Language Models for Underwater Bioacoustic Spectrogram Analysis","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.05703","url_pdf":"https://arxiv.org/pdf/2509.05703v1","authors":"[\"Ragib Amin Nihal\",\"Benjamin Yen\",\"Takeshi Ashizawa\",\"Kazuhiro Nakadai\"]","published":"2025-09-06T12:36:59Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.IR\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}