Guest Editorial: Special Issue on Performance Evaluation in Computer Vision

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Guest Editorial: Special Issue on Performance Evaluation in Computer Vision

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Guest Editorial: Special Issue on Performance Evaluation in Computer Vision

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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