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Special Section: CIARP-24
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DOI：10.1016/j.patrec.2025.11.039.png)
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Abstract
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The Iberoamerican Congress on Pattern Recognition (CIARP) is a well-established scientific event, endorsed by the International Association for Pattern Recognition (IAPR), that focuses on all aspects of pattern recognition, computer vision, artificial intelligence, data mining, and related areas. Since 1995, it has provided an important forum for researchers in IberoAmerica and beyond for presenting ongoing research, scientific results, and experiences on mathematical models, computational methods, and their applications in areas such as robotics, industry, health, space exploration, telecommunications, document analysis, and natural language processing. CIARP has helped strengthening regional cooperation and had contributed to the development of emerging research groups across Iberoamerica. The 27th edition, was held at Universidad Católica del Maule in Talca, Chile, from November 26–29, 2024, and comprised an engaging four-day program of single-track sessions, tutorials, and invited keynotes. I had the privilege to be its Program Chair. As guest editor of this Special Section, I am pleased to introduce fully extended and peer-reviewed versions of the two papers that were awarded best paper prizes in CIAPR-24. In the first one, from Argentina and Uruguay, [1] expand their work to describe a multi-sensor approach for automatic precipitation remote sensing detection using Conditional GANs and Recurrent Networks of special relevance in places where precipitations are not very common events. They integrate satellite infrared brightness temperature (IR-BT) with lighting temporal signals and argue that their proposed architecture achieves better precision than alternative methods. They suggest that their results have potential applications in cyanobacteria bloom event prediction and to help setting social policies for water resource management. This is a good example on how pattern recognition research may have a clear impact. In the second paper, from Chile, [2] extend their previous work and consider the problem of dealing with Out-Of-Distribution (ODD) data in text classification. They propose a new method, BBMOE, based on bimodal beta mixture distribution that fine-tunes pre- trained models using labeled OOD data with a bimodal Beta mixture distribution regularization that enhances differentiation between near-OOD and far-OOD data in multi-class text classification. Their results show improvements over the state-of-the-art for various datasets. We thank the authors and the reviewers for their thorough work and hope that you enjoy reading these papers and perhaps consider submitting work to a future CIARP.
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