2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

doi:10.1109/iccvw54120.2021

Cited by 5 publications

(1 citation statement)

References 56 publications

(56 reference statements)

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“…The second competition was the Masked Face Recognition Challenge [34] organized within the Face Bio-Metrics Under COVID? Masked Face Recognition (MFR) Workshop, one of the IEEE/CVF International Conference on Computer Vision Workshops [115]. The competition included three test sets and used an online model testing system and provided a detailed evaluation of the submitted face recognition models.…”

Section: Masked Face Recognition Competitionsmentioning

confidence: 99%

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

Eyiokur¹,

Kantarcı²,

Erakin³

et al. 2022

Preprint

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The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting facehand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field.Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

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Section: Masked Face Recognition Competitionsmentioning

confidence: 99%

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

Eyiokur¹,

Kantarcı²,

Erakin³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

A survey on computer vision based human analysis in the COVID-19 era

Eyiokur

Kantarcı

Erakin

et al. 2023

Image and Vision Computing

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A Distinguishable Pseudo-Feature Synthesis Method for Generalized Zero-Shot Learning

Jia

Liu

et al. 2022

Computational Intelligence and Neuroscience

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Generalized zero-shot learning (GZSL) aims to classify seen classes and unseen classes that are disjoint simultaneously. Hybrid approaches based on pseudo-feature synthesis are currently the most popular among GZSL methods. However, they suffer from problems of negative transfer and low-quality class discriminability, causing poor classification accuracy. To address them, we propose a novel GZSL method of distinguishable pseudo-feature synthesis (DPFS). The DPFS model can provide high-quality distinguishable characteristics for both seen and unseen classes. Firstly, the model is pretrained by a distance prediction loss to avoid overfitting. Then, the model only selects attributes of similar seen classes and makes sparse representations based on attributes for unseen classes, thereby overcoming negative transfer. After the model synthesizes pseudo-features for unseen classes, it disposes of the pseudo-feature outliers to improve the class discriminability. The pseudo-features are fed into a classifier of the model together with features of seen classes for GZSL classification. Experimental results on four benchmark datasets verify that the proposed DPFS has GZSL classification performance better than that in existing methods.

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2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Cited by 5 publications

References 56 publications

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

A survey on computer vision based human analysis in the COVID-19 era

A Distinguishable Pseudo-Feature Synthesis Method for Generalized Zero-Shot Learning

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