PT Unknown
AU Nil Ballus
   Bhalaji Nagarajan
   Petia Radeva
TI Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition
BT 10th Iberian Conference on Pattern Recognition and Image Analysis
PY 2022
VL 13256
DI 10.1007/978-3-031-04881-4_52
DE Self-supervised; Contrastive learning; Food recognition
AB Self-supervised Learning has been showing upbeat performance in several computer vision tasks. The popular contrastive methods make use of a Siamese architecture with different loss functions. In this work, we go deeper into two very recent state of the art frameworks, namely, SimSiam and Barlow Twins. Inspired by them, we propose a new self-supervised learning method we call Opt-SSL that combines both image and feature contrasting. We validate the proposed method on the food recognition task, showing that our proposed framework enables the self-learning networks to learn better visual representations.
ER