SegIns: A simple extension to instance discrimination task for better localization learning

Baydar M., AKBAŞ E.

Journal of Visual Communication and Image Representation, cilt.100, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 100
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.jvcir.2024.104122
  • Dergi Adı: Journal of Visual Communication and Image Representation
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC
  • Anahtar Kelimeler: Contrastive learning, Instance discrimination task, Non-contrastive learning, Object detection, Self-supervised learning, Semantic segmentation
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Recent self-supervised learning methods, where instance discrimination task is a fundamental way of pretraining convolutional neural networks (CNN), excel in transfer learning performance. Even though instance discrimination task is a well suited pretraining method for classification with its image-level learning, lack of dense representation learning makes it sub-optimal for localization tasks such as object detection. In this paper, we aim to mitigate this shortcoming of instance discrimination task by extending it to jointly learn dense representations alongside image-level representations. We add a segmentation branch parallel to the image-level learning to predict class-agnostic masks, enhancing location-awareness of the representations. We show the effectiveness of our pretraining approach on localization tasks by transferring the learned representations to object detection and segmentation tasks, providing relative improvements by up to 1.7% AP on PASCAL VOC and 0.8% AP on COCO object detection, 0.8% AP on COCO instance segmentation and 3.6% mIoU on PASCAL VOC semantic segmentation respectively.