The paper introduces an automated high‑throughput microscopy system for melissopalynology that combines H∞ robust mechanical control with deep learning pipelines. It uses U^2‑Net for salient object detection and a DINOv2 Vision Transformer trained via deep metric learning for pollen grain classification, augmented with Gradient‑Weighted Attention for interpretable texture annotations. The system reports a 95.8% classification recall and at least a six‑fold speedup over manual expert analysis.
By J. Staforelli-Vivanco, R. Jofr\'e, P. Coelho, I. Sanhueza, L. Viafora, C. Toro, J. Troncoso, M. Rondanelli-Reyes, I. Lamas, Andy Banegas-Medina, Isis-Yelena Montes, B. Mu\~noz-Cepeda, V. Salamanca-Levi, M. Gonz\'alez-Ortiz, E. Vera
arXiv:2601.03163v2 Announce Type: replace
Abstract: Background and Objective: Precise and scalable instance segmentation of cell nuclei is a fundamental prerequisite for computational pathology, yet...
By Mat\v{e}j Pek\'ar, V\'it Musil, Rudolf Nenutil, Petr Holub, Tom\'a\v{s} Br\'azdil
arXiv:2606. 14562v1 Announce Type: cross Abstract: Sociable weaver nests function as complex ecological structures offering thermoregulatory microhabitats and sustaining diverse species; however, datasets used in prior studies lack fine-grained 3D structural detail.
By Constanza A. Molina Catricheo, Simon Boeder, Ting-Jia Guo, Giacomo May, Cl\'ement Berthelot, Devis Tuia, Friedrich Fedor Reinhard, Fabio Remondino, Benjamin Risse
AtlasPatch is a scalable, high‑throughput whole‑slide image preprocessing method that uses a foundation‑model‑based tissue detector operating at thumbnail resolution. By updating only 0.076% of the SAM2 model weights and leveraging a curated dataset of 30,000 thumbnail‑mask pairs, it generates accurate tissue masks and directly produces patch coordinates at the desired magnification, eliminating repeated patch‑level inference. The approach achieves 0.986 precision, is up to 16× faster than existing deep‑learning methods, and maintains downstream multiple‑instance learning performance across six slide‑level classification tasks.
By Ahmed Alagha, Christopher Leclerc, Yousef Kotp, Omar Metwally, Calvin Moras, Peter Rentopoulos, Ghodsiyeh Rostami, Bich Ngoc Nguyen, Jumanah Baig, Abdelhakim Khellaf, Vincent Quoc-Huy Trinh, Rabeb Mizouni, Hadi Otrok, Jamal Bentahar, Mahdi S. Hosseini
AgriCountDINO is a parameter‑efficient, exemplar‑guided framework that jointly counts and localizes plants and their organs by conditioning frozen multiscale DINOv3 features on exemplar appearance and size, then decoding them into target points. It introduces missed‑object recovery and exemplar‑adaptive point NMS to improve detection accuracy. With only 8.4 M trainable parameters, it achieves a three‑shot MAE of 11.92 on the TPC‑268 benchmark and a zero‑shot MAE of 14.25 on unseen generic object categories in FSC‑147, outperforming previous methods without target‑domain training.
By Shengjie Guo, Xin Li, Borjana Arsova, Hanno Scharr, Silvio Salvi
arXiv:2602.19736v3 Announce Type: replace
Abstract: Diffusion models now give the best perceptual quality in super-resolution (SR), but their architecture and training confine them to small fixed cro...
By Shoukun Sun, Zhe Wang, Xiang Que, Jiyin Zhang, Xiaogang Ma
arXiv:2607. 14509v1 Announce Type: cross Abstract: This paper describes DS@GT ARC's third-place solution to the PlantCLEF 2026 challenge on multi-species plant identification in vegetation quadrat images, where systems must predict every species present in high-resolution (~3000 x 3000 pixel) plot photographs while training only on single-label images of individual plants.
By Alper Erten, Murilo Gustineli, Adrian Cheung
arXiv:2609.30866v1 Announce Type: cross
Abstract: In scanning microscopy, drift causes the specimen to be sampled at positions displaced from the nominal probe positions. This displacement alters the...
By Sangjoon Lee, William Millsaps, Dasol Yoon, Caitlyn Obrero, Guoliang Hu, Corrie Barnes, Cedric Lim, Andrew Barnum, Arthur R. C. McCray, Colin Ophus
arXiv:2608. 03023v1 Announce Type: cross Abstract: Remote sensing semantic segmentation is hindered by costly pixel-level annotations, motivating training-free open-vocabulary methods.
By Changhao Zhao, Haoxiang Li, Yuke Li, Hai Liu, LingLin Zeng
arXiv:2409.13568v3 Announce Type: replace
Abstract: Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing method...
By Foivos I. Diakogiannis, Zheng-Shu Zhou, Jeff Wang, Gonzalo Mata, Dave Henry, Roger Lawes, Amy Parker, Peter Caccetta, Suzanne Furby, Rodrigo Ibata, Ondrej Hlinka, Jonathan Richetti, Kathryn Batchelor, Chris Herrmann, Andrew Toovey, John Taylor
arXiv:2607. 26743v1 Announce Type: cross Abstract: Artificial intelligence has shown considerable potential for archaeological applications, yet its use in zooarchaeology remains limited, particularly for the identification of avian skeletal remains.
By Nevio Dubbini, Lisa Yeomans, Marco Pavia, Ramazan Parmaksiz, Ayse Atas Hooglugt, Gabriele Gattiglia, Beatrice Demarchi
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
By David-Alexandre Duclos, William Guimont-Martin, Gabriel Jeanson, Arthur Larochelle-Tremblay, Martine Lapointe, Th\'eo Defosse, Fr\'ed\'eric Moore, Philippe Nolet, Fran\c{c}ois Pomerleau, Philippe Gigu\`ere