arXiv Computation and Language September 4, 2026 By Nazim-E-Alam, Tarek Rahman, Md Kishor Morol

IchthyoNoma: Nomenclature and Context Sensitivity of Zero-Shot Biological Vision--Language Models for Bangladeshi Freshwater Fish Recognition

Read the original on arXiv Computation and Language →

The paper audits zero‑shot vision‑language models—CLIP, BioCLIP, BioCLIP2, and a multilingual Jina CLIP v2—on 10,321 images of seven Bangladeshi freshwater fish species. BioCLIP2 achieves the highest accuracy (72.36% on BFF‑15 with English common names and 68.91% on SylFishBD with scientific names), while generic CLIP lags far behind (25.15% and 14.40%). The study also shows that Bengali prompts perform poorly, that Jina CLIP partially improves Bengali discrimination, and that image context (blur, masking) affects performance differently across species.

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