Multi-level Representation Fusion Methods based on Weakly Supervised Learning

• Grant number: 21/01870-5
• Support Opportunities: Scholarships in Brazil - Master
• Effective date (Start): April 01, 2021
• Effective date (End): August 31, 2022
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Acordo de Cooperação: Microsoft Research
• Principal Investigator: Fabio Augusto Faria
• Grantee: Luiz Henrique Buris
• Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
• Host 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

Several machine learning techniques have relied on large labeled data sets to construct predictive models and solving supervised learning tasks. The use of deep learning techniques can be highlighted, since it have been broadly and successfully used in various domains. On the other hand, in many circumstances, the labeled sets are unavailable or insufficient to train effective supervised models. Such scenarios have been mainly addressed by unsupervised learning techniques, which consider the unlabeled data to learn about its structure. However, the use of completely unsupervised methods still remains a research challenge in many scenarios and situations. A promising solution is based on the use of weakly supervised approaches, capable of performing effective learning tasks based on incomplete or inaccurate labeled sets. The main objective of this master's project is the implementation of a method of multi-level representation fusion through the use of weakly supervised learning technique for multimedia recognition tasks. I (AU)