Guest Editorial: Special Issue on Traditional Computer Vision in the Age of Deep Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00388105" target="_blank" >RIV/68407700:21730/24:00388105 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s11263-024-02062-2" target="_blank" >https://doi.org/10.1007/s11263-024-02062-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11263-024-02062-2" target="_blank" >10.1007/s11263-024-02062-2</a>

Result language

angličtina

Original language name

Guest Editorial: Special Issue on Traditional Computer Vision in the Age of Deep Learning

Original language description

In the last 5–10 years we have witnessed that deep learning has revolutionized Computer Vision, conquering the main scene in most top-tier conferences and journals. However, several problems and topics for which deep-learned solutions are currently not preferable over classical ones exist, that typically involve a strong mathematical model (e.g., camera calibration and structure-from-motion). This special issue collects contributions related to algorithms and methodologies that address Computer Vision problems in a “traditional” or “classic” way, in the sense that analytical/explicit models are deployed, as opposed to learned/neural ones. A particular focus is given to traditional approaches that perform better than neural ones (for instance, in terms of generalization across different domains) or that, although performing sub-par, provide clear advantages with respect to deep learning solutions (for instance, in terms of efforts to collect data, computational requirements, power consumption or model compactness). We hope this special issue can inspire the reader towards critical discussions about preferring a traditional solution rather than a deep learning approach, igniting relevant questions about how to bridge the gap between learning and classic knowledge, as well as ethical implications of deep learning approaches in comparison to traditional ones.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů