Guest Editorial: Special Issue on Performance Evaluation in Computer Vision

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00357565" target="_blank" >RIV/68407700:21730/21:00357565 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s11263-021-01455-x" target="_blank" >https://doi.org/10.1007/s11263-021-01455-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11263-021-01455-x" target="_blank" >10.1007/s11263-021-01455-x</a>

Result language

angličtina

Original language name

Guest Editorial: Special Issue on Performance Evaluation in Computer Vision

Original language description

As the field of computer vision is growing and maturing, performance evaluation has become essential. Most sub-areas of computer vision now have established datasets and benchmarks allowing quantitative evaluation and comparison of current methods. In addition, new benchmarks often stimulate research into the particular challenges presented by the data. Conversely, important areas lacking high-quality datasets and benchmarks might not receive adequate attention by researchers. The deep learning revolution has made datasets and performance evaluation even more important. Learning-based methods not only require large, well-designed training datasets but also well-defined loss functions, which are usually designed to optimize established performance measures. This creates an implicit bias based on the availability of datasets and the definition of performance metrics.

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

2021

Confidentiality

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