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arXiv AI

FSVL: Taxonomic Fast-Slow Vision-to-Language Collaborative Framework for Interpretable Confusable Fish Species Identification and Knowledge Generation in the Middle Yangtze River

The paper introduces FSVL, a fast‑slow vision‑to‑language framework for identifying 51 freshwater fish species in the Middle Yangtze River Basin. A lightweight ResNet50 fast path first classifies images, while a slow path using YOLOv8n refines predictions on confusing species by focusing on key morphological regions. The system couples visual recognition with a retrieval‑augmented Qwen3‑14B language model, achieving 100 % accuracy on a 607‑image test set and improving knowledge‑generation metrics such as scientific name accuracy and ROUGE‑L.

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arXiv AI
2d ago

How Finely Can Vision-Language Models See Biology? Bio-VQA: A Benchmark for Taxonomic, Morphological, Spatial and Individual Reasoning

Bio‑VQA is a new, large-scale visual question answering benchmark for biology, comprising 1.86 million questions over 217 k images from 11 datasets. It tests four key aspects of biological visual understanding—taxonomic reasoning, attribute grounding, spatial reasoning, and individual re‑identification—using a graded scoring system that rewards near‑misses more than distant ones. Evaluation of 18 open‑weight vision‑language models shows that general visual capabilities do not transfer well to biological tasks, with markedly lower accuracy on counting, taxonomy, trait measurement, and re‑identification compared to everyday object benchmarks.

By S M Rayeed, Qian Ma, Charles V. Stewart, Yao Ma
llmsnlpmultimodalbenchmarkssafety
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arXiv Computation and Language
Sep 4

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

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.

By Nazim-E-Alam, Tarek Rahman, Md Kishor Morol
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arXiv Machine Learning
Sep 22

Vision Transformers versus convolutional neural networks for fine-grained orchid genus identification in a species-rich, data-poor flora: a controlled benchmark on the Orchidaceae of New Guinea

The study benchmarks Vision Transformers (ViTs) against convolutional neural networks (CNNs) for fine‑grained orchid genus identification in New Guinea’s species‑rich, data‑poor flora. Using a two‑stage system that first predicts genus and then retrieves similar species images, the authors fine‑tuned four pretrained backbones on 16,701 photographs from 120 genera and 1,350 species. The self‑supervised ViT DINOv2 achieved the highest genus accuracy (macro top‑1 66.9 %) and outperformed both CNNs and a domain‑matched pretrained ViT, demonstrating strong species retrieval and open‑set detection capabilities.

By Reza Saputra, Diah Harnoni Apriyanti, Andr\'e Schuiteman, Kurt Metzger, Ashley Field, Katharina Nargar, William Edwards
llmsragfine-tuningbenchmarks
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arXiv AI
Jul 17

Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI

arXiv:2603. 16351v2 Announce Type: replace-cross Abstract: Accurate taxonomic identification of parasitoid wasps within the superfamily Ichneumonoidea is essential for biodiversity assessment, ecological monitoring, and biological control programs.

By Joao Manoel Herrera Pinheiro, Gabriela Do Nascimento Herrera, Alvaro Doria Dos Santos, Luciana Bueno Dos Reis Fernandes, Ricardo V. Godoy, Eduardo A. B. Almeida, Helena Carolina Onody, Marcelo Andrade Da Costa Vieira, Angelica Maria Penteado-Dias, Marcelo Becker
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arXiv Computer Vision
5d ago

FieldGuideRAG: Image-Grounded GraphRAG for Fine-Grained Recognition with Vision-Language Models

FieldGuideRAG is a training‑free GraphRAG approach that uses a frozen vision‑language model to recognize fine‑grained species by comparing a query image to photographs stored on each species node in a knowledge graph. The graph links species to relatives, geographic range, and ecological facts, and the system fuses verification scores with range edges to answer organism‑related questions. On datasets such as CUB‑200 and iNaturalist, FieldGuideRAG’s use of node photographs improves verification over species names, surpasses a SigLIP retriever by up to 16 points, and achieves up to 89.5% accuracy on ecological questions.

By Jiacheng Ding, Yanliang Qi, Cong Guo, Peihua Mai
llmsmultimodal
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