Self-supervised for Visual Representation Learning

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Introduction

Supervised learning tasks require a huge amount of annotated data, that is usually hard and expensive to label.
Current advancements in computer vision are moving the field toward a category of algorithms called self-supervised learning, for learning visual representations directly from unsupervised data. The goal of these techniques is to leverage non-labelled data and learn key visual representations that are useful and transferable to a set of supervised tasks.

The goal of this thesis is to investigate current algorithms for learning visual representations using self-supervised approaches, and apply them to both unsupervised and supervised tasks like anomaly detection, video action detection, and objects detection, with a low annotated-data regime.

Planned Activities

  • Research current self-supervised learning algorithms for computer vision.
  • Develop the most promising self-supervised algorithms.
  • Apply, evaluate, and benchmark the developed algorithms on a set of downstream tasks.

Who we’re looking for

Students that are about to get their Master Degree in: Computer Science, Computer Engineering, Mechatronic Engineering, Electronic
Engineering, Mathematical Engineering, Mathematics, Physics.

Skills: Proficiency in Python; Basic knowledge of Machine learning and Deep learning; Experience with at least one Deep learning framework (e.g. TensorFlow, Pytorch); Good knowledge of linear algebra.

Duration of this Projects: 6-8 months

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Contact Us

Directly by email to: [email protected]

By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467