arXiv Machine Learning July 20, 2026 By Piotr S. Maci\k{a}g, Monika Maci\k{a}g, Magdalena Majdan

Hierarchical Specialised Ensembles for Classification of Zebrafish Phenotypes Using the Selected Image Recognition Methods

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arXiv:2607. 15698v1 Announce Type: cross Abstract: We propose and evaluate three hierarchical ensemble setups for zebrafish phenotype classification from embryo images.

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computer-vision

arXiv Machine Learning

Jul 8

Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers

arXiv:2112. 02353v3 Announce Type: replace-cross Abstract: Hierarchical classification aims to sort the object into a hierarchical structure of categories.

By Renzhen Wang, De cai, Kaiwen Xiao, Xixi Jia, Xiao Han, Deyu Meng

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

Aug 11

Modern Backbones Improve Multi-task DETR for Mammography Classification and Lesion Localization

arXiv:2608. 09801v1 Announce Type: cross Abstract: Joint exam-level prediction and candidate-region localization may improve the usefulness of AI support in mammography.

By Dinh Tan Nguyen, Quang-Hien Kha, Le-Hoang Nguyen, Minh-Toan Dinh, Xuan-Huy Nguyen, Dac Phu Ho, Cao Truong Tran, Sai Ho Ling, Lan T Ho-Pham, Liem Pham, Nguyen Quoc Khanh Le

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 AI

Jul 31

Multimodal fusion of visual and morphometric features for avian bone classification

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

multimodal

arXiv Computer Vision

Sep 3

Efficient Passive Acoustic Monitoring of Killer Whales Using a Two-Stage Detection and Ecotype Classification Cascade

The paper presents a lightweight ResNet-based two-stage cascade for passive acoustic monitoring of killer whales. First, it detects vocalizations, then it classifies confident detections into five eastern North Pacific ecotypes, abstaining on ambiguous calls. The pipeline achieves high macro‑F1 scores on the DCLDE 2027 dataset and improves real‑time inference speed, while active learning adapts the detector to new acoustic environments.

By Daniela Ruiz, Manuel Castellote, Zhongqi Miao, Carl Chalmers, Bruno Demuro, Rahul Dodhia, Pablo Arbelaez, Juan M. Lavista

rag benchmarks