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Weakly supervised learning strategies through Rank-based measures

Grant number: 19/04754-6
Support type:Scholarships in Brazil - Master
Effective date (Start): April 01, 2019
Effective date (End): March 31, 2021
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Cooperation agreement: Microsoft Research
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:João Gabriel Camacho Presotto
Home Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Company:Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Instituto de Geociências e Ciências Exatas (IGCE)
Associated research grant:17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert, AP.PITE

Abstract

Unsupervised contextual measures can effectively encode useful information regarding the dataset structure. Rank-based measures can properly model situations which indicates similarity relationships with a high degree of confidence. In such situations, the objects involved are included in a expanded training set, allowing a weakly supervised learning strategies.