A comparative analysis of rank correlation measures for weakly-supervised learning

• Grant number: 19/11104-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2019
• End date: August 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Agreement: Microsoft Research
• Principal Investigator: Daniel Carlos Guimarães Pedronette
• Grantee: Nikolas Gomes de Sá
• 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

The rank correlation measures represent an effective way to encode contextual similarity information. Recently, such measures have been successfully exploited in various unsupervised learning tasks.In this scenario, this project considers the hypothesis that these measures can also be applied in weakly supervised learning tasks. The main idea consists in expanding small training sets through relationships with high values computed rank correlation measures. In this way, the main objective is to conduct a comparative study of different rank correlation measures which can be applied on weakly supervised learning methods.

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)

DE SA, NIKOLAS GOMES; VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; FARINELLA, GM; RADEVA, P; BRAZ, J; BOUATOUCH, K. A Multi-level Rank Correlation Measure for Image Retrieval. VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 5: VISAPP, v. N/A, p. 9-pg., 2021-01-01. (17/25908-6, 18/15597-6, 19/11104-8)

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)