A Review on Self-Supervised Learning Methods in Computer Vision

Year 2024, Volume: 12 Issue: 2, 1136 - 1165, 29.04.2024

Serdar Alasu , Muhammed Fatih Talu

https://doi.org/10.29130/dubited.1201292

Abstract

Although deep learning models have achieved great success in computer vision tasks such as image classification, object detection, image segmentation in the last decade, a large amount of labeled data requires in the training of these models, which are in a supervised learning approach. Therefore, in recent years, there has been an increased interest in self-supervised learning methods that can learn generalizable image representations by utilizing large-scale unlabeled data without the need for manually labeled data by humans. In this study, self-supervised learning methods used in computer vision tasks are comprehensively reviewed and categorization of self-supervised learning methods is provided. Performance comparisons of the reviewed self-supervised learning methods for image classification, object detection and image segmentation target tasks are presented. Finally, problematic issues in current methods are discussed and potential research topics are suggested for future studies.

Keywords

Computer Vision, Self-Supervised Learning, Contrastive Learning

Bilgisayarlı Görüde Öz-Denetimli Öğrenme Yöntemleri Üzerine Bir İnceleme

Abstract

Derin öğrenme modelleri son on yılda görüntü sınıflandırma, nesne tespiti, görüntü bölütleme vb. bilgisayarlı görü görevlerinde büyük başarılar elde etmelerine rağmen denetimli öğrenme yaklaşımında olan bu modellerin eğitiminde büyük miktarda etiketli veriye ihtiyaç duyulmaktadır. Bu nedenle, son yıllarda insanlar tarafından manuel olarak etiketlenen veriye ihtiyaç duymadan etiketsiz büyük boyutlu veriden faydalanarak genelleştirilebilir görüntü temsillerini öğrenebilen öz-denetimli öğrenme yöntemlerine ilgi artmıştır. Bu çalışmada, bilgisayarla görü görevlerinde kullanılan öz denetimli öğrenme yöntemleri kapsamlı bir şekilde incelenmiş ve öz denetimli öğrenme yöntemlerinin kategorizasyonu sağlanmıştır. İncelenen öz-denetimli öğrenme yöntemlerinin görüntü sınıflandırma, nesne tespiti ve görüntü bölütleme hedef görevleri için performans karşılaştırmaları sunulmuştur. Son olarak, mevcut yöntemlerdeki sorunlu hususlar tartışılmakta ve gelecek çalışmalar için potansiyel araştırma konuları önerilmektedir.

Keywords

Bilgisayarlı Görü, Öz-Denetimli Öğrenme, Karşılaştırmalı Öğrenme

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