arXiv Computer Vision

AgentCount: An Agentic Workflow for Traceable Colony Counting and Insight Generation from Agar Plate Images

arXiv AI
1d 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
arXiv Machine Learning
Jul 21

SGMCE: Segment-Grounded Morphological Concept Explanation for Malaria Parasite Species Identification in Thick Blood Smears

arXiv:2607. 16324v1 Announce Type: cross Abstract: Malaria diagnosis in endemic regions depends on species-level identification of Plasmodium parasites in thick blood smears, but deep learning detectors classify detections without providing morphological evidence for their predictions, limiting the ability of microscopists to audit those predictions at the case level.

By Ahmed Tahiru Issah, Charles B. Delahunt, Carine Mukamakuza
arXiv AI
Sep 12

Can Edge-Deployable Vision-Language Models Identify Species?

arXiv:2609. 11916v1 Announce Type: new Abstract: Camera traps often run in the field on edge hardware with limited or no connectivity, making small, locally-deployable vision-language models (VLMs) -- not frontier-scale ones -- the practically relevant class to evaluate for species identification.

By William Zhou, Mayukha Siripuram, Xiao Yan, Ziqi Liu, Yi Ding
arXiv AI
Jun 2

Agentic-J: An AI Agent for Biological Microscopy Image Analysis

arXiv:2606. 02080v1 Announce Type: cross Abstract: Biological image analysis increasingly demands integration across heterogeneous tools, programming environments, and domain knowledge that few researchers can command simultaneously.

By Lukas Johanns, Marilin Moor, Davide Panzeri, Yu Zhou, Xinyi Chen, Nora F. K. Pauly, Zixuan Pan, Matthias Gunzer, Andreas M\"uller, Yiyu Shi, Hedi Peterson, Jianxu Chen