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Towards open-world recognition: : Critical problems and challenges

Authors:

Junlan ChenAuthors Info & Claims

Volume 143, Issue C

https://doi.org/10.1016/j.engappai.2025.110042

Published: 20 February 2025 Publication History

Abstract

With the emergence of rich classification models and high computing power, recognition systems are widely used in various fields. Unfortunately, as the scale of open systems increases, the assumption of a closed-world will lead to the failure of fragile models because almost all machine learning based recognition algorithms are evaluated as implicit “closed-set”. Comparing with classical methods, open-world learning can address concerns in dynamic environments where the input data (size, category, etc.) is changing rapidly. Nevertheless, there still lacks a thorough review of recent advances in open-world recognition. Therefore, we provide an in-depth discussion of open-world recognition based on some recent works. First, we propose a learning framework for open-world recognition, and analyze the challenges from three aspects: domain shift,limits on the amount of labeled data and perception scene with dynamic changes. Secondly, we evaluate the current state of the art, summarize the intersection of various methods and find out some existing problems. Finally, we discuss the limitations of current procedures and new technologies as well as future directions in order to make meaningful progress. This article will help researchers understand Open-world learning and the possibilities of extending research into appropriate areas.

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Index Terms

Towards open-world recognition: Critical problems and challenges
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 143, Issue C

Mar 2025

1535 pages

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Elsevier Ltd.

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Published: 20 February 2025

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