arXiv AI October 26, 2026 By Meng Liang, Fucheng Wang, Lang Zhao, Jun Chen, Ziying Li, Yuxiang Zhao, Haozhuang Chi, Wenkai Fang, Qiong Zhou, Junlin Yang, Jinming Wu

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

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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

llms nlp multimodal benchmarks safety

arXiv Machine Learning

Jun 25

Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

arXiv:2606. 25989v1 Announce Type: cross Abstract: Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy.

By Dan Zimmerman, Dimitris A. Pados, George Sklivanitis

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

llms multimodal safety

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

llms rag fine-tuning benchmarks

arXiv Computer Vision

Sep 22

Enhancing Shrimp Disease Detection via Deep Learning and Data Refinement for Resilient Aquaculture

arXiv:2609.23397v1 Announce Type: new Abstract: Shrimp diseases continue to cause devastating losses in the aquaculture industry, driving a critical need for robust, automated detection. This work co...

By Vinh Canh-Thanh Truong, Hai-Binh Pham, Ngoc Hong Tran

llms fine-tuning

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

computer-vision safety