Weakly supervised learning strategies through Rank-based measures

• Grant number: 19/04754-6
• Support Opportunities: Scholarships in Brazil - Master
• Start date: April 01, 2019
• End date: March 31, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: Microsoft Research
• Principal Investigator: Daniel Carlos Guimarães Pedronette
• Grantee: João Gabriel Camacho Presotto
• Host 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.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PRESOTTO, JOAO GABRIEL CAMACHO; DOS SANTOS, SAMUEL FELIPE; VALEM, LUCAS PASCOTTI; FARIA, FABIO AUGUSTO; PAPA, JOAO PAULO; ALMEIDA, JURANDY; PEDRONETTE, DANIEL CARLOS GUIMARAES. Weakly supervised learning based on hypergraph manifold ranking?. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v. 89, p. 12-pg., 2022-10-25. (18/15597-6, 18/23908-1, 17/25908-6, 19/04754-6, 20/11366-0)

CAMACHO PRESOTTO, JOAO GABRIEL; VALEM, LUCAS PASCOTTI; DE SA, NIKOLAS GOMES; GUIMARAES PEDRONETTE, DANIEL CARLOS; PAPA, JOAO PAULO; IEEE COMP SOC. Weakly Supervised Learning through Rank-based Contextual Measures. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), v. N/A, p. 8-pg., 2021-01-01. (14/12236-1, 19/11104-8, 18/15597-6, 19/04754-6, 20/11366-0)

PRESOTTO, JOAO GABRIEL CAMACHO; VALEM, LUCAS PASCOTTI; DE SA, NIKOLAS GOMES; PEDRONETTE, DANIEL CARLOS GUIMARAES; PAPA, JOAO PAULO. Weakly supervised classification through manifold learning and rank-based contextual measures. Neurocomputing, v. 589, p. 18-pg., 2024-04-24. (19/11104-8, 20/11366-0, 14/12236-1, 18/15597-6, 19/04754-6, 17/25908-6)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

PRESOTTO, João Gabriel Camacho. Rank-based weakly supervised machine learning methods. 2021. Master's Dissertation - Universidade Estadual Paulista (Unesp). Instituto de Geociências e Ciências Exatas. Rio Claro Rio Claro.