aiqbench analysis
This paper presents ReLEAP, a Python framework for merging heterogeneous image datasets under a shared label taxonomy, tested on fifteen plant datasets for precision agriculture. Its main contribution is practical: it shows that unified access and semi-supervised or weakly supervised annotation upcycling can extend the usable training data, though the gains are reported from the authors' own experiments. Readers building vision models in data-scarce domains should watch how dataset characteristics affect when this approach works.
ReLEAP: Upcycling Vision Datasets through Cross-Task Unification and Semi-Supervised Annotation
arXiv:2610.32289v1 Announce Type: cross Abstract: Developing robust vision models requires large amounts of annotated data, especially for tasks involving fine-grained class distinctions under variable environmental conditions. Even in domains with numerous existing datasets, differences in annotation formats, label definitions and targeted learning tasks hinder data reuse and integration. To address this challenge, we introduce ReLEAP, a scalable Python framework for unifying heterogeneous image datasets through data-source abstraction, systematic reuse of annotations across learning tasks, and mapping of labels to a shared hierarchical taxonomy. We demonstrate its applicability in precision agriculture by integrating fifteen diverse datasets covering a broad range of plant species and annotation types. Beyond unification, ReLEAP enables systematic recycling of existing data and provides a robust foundation for learning-based annotation upcycling. Leveraging this foundation, we conduct extensive cross-dataset and fine-tuning experiments to investigate how domain-specific models trained on unified data and foundation models can be used to automatically generate higher-level annotations under unsupervised, weakly supervised and semi-supervised settings, and how dataset characteristics influence their effectiveness. Our results show that unified data access and annotation upcycling substantially expand the pool of reusable training data and annotations while improving data transparency, enabling more sustainable and resource-efficient use of existing datasets. We release the framework and trained models for academic use to support accessible and collaborative research, particularly in data-scarce and heterogeneous domains.
Abstract:Developing robust vision models requires large amounts of annotated data, especially for tasks involving fine-grained class distinctions under variable environmental conditions. Even in domains with numerous existing datasets, differences in annotation formats, label definitions and targeted learning tasks hinder data reuse and integration. To address this challenge, we introduce ReLEAP, a scalable Python framework for unifying heterogeneous image datasets through data-source abstraction, systematic reuse of annotations across learning tasks, and mapping of labels to a shared hierarchical taxonomy. We demonstrate its applicability in precision agriculture by integrating fifteen diverse datasets covering a broad range of plant species and annotation types. Beyond unification, ReLEAP enables systematic recycling of existing data and provides a robust foundation for learning-based annotation upcycling. Leveraging this foundation, we conduct extensive cross-dataset and fine-tuning experiments to investigate how domain-specific models trained on unified data and foundation models can be used to automatically generate higher-level annotations under unsupervised, weakly supervised and semi-supervised settings, and how dataset characteristics influence their effectiveness. Our results show that unified data access and annotation upcycling substantially expand the pool of reusable training data and annotations while improving data transparency, enabling more sustainable and resource-efficient use of existing datasets. We release the framework and trained models for academic use to support accessible and collaborative research, particularly in data-scarce and heterogeneous domains.
Accepted at Asian Conference on Computer Vision (ACCV) 2026. Code and models available at this https URL
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2610.32289 [cs.CV]
(or arXiv:2610.32289v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.32289 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 334 words