Matteo Latino
Self-supervised contrastive learning with hard positive mining for online action detection.
Rel. Francesco Vaccarino. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
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Abstract: |
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. |
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Relators: | Francesco Vaccarino |
Academic year: | 2021/22 |
Publication type: | Electronic |
Number of Pages: | 107 |
Subjects: | |
Corso di laurea: | Corso di laurea magistrale in Data Science And Engineering |
Classe di laurea: | New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING |
Aziende collaboratrici: | ADDFOR S.p.A |
URI: | http://webthesis.biblio.polito.it/id/eprint/21150 |
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