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Mining persons, objects and places of interest from heterogeneous data sources

Grant number: 19/15825-1
Support type:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): August 01, 2019
Effective date (End): November 30, 2023
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Anderson de Rezende Rocha
Grantee:Gabriel Capiteli Bertocco
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events, AP.TEM
Associated scholarship(s):22/02299-2 - Self-supervised learning for biometrics and beyond, BE.EP.DD

Abstract

In this research, we aim at designing a framework to rank the frequency of the appearance of different individuals, objects or places based on text and visual information assets related to an event for further analysis. Moreover, we intend to (1) design and develop appropriate methods to search for some possible classes of interest (possible suspects, places, or objects of interest), while rejecting non-interesting classes/objects; and (2) develop human identification methods (e.g., face recognition) for such challenging and specific scenarios to identify possible suspects in an event. (AU)

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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)
BERTOCCO, GABRIEL C.; ANDALO, FERNANDA; ROCHA, ANDERSON. Unsupervised and Self-Adaptative Techniques for Cross-Domain Person Re-Identification. IEEE Transactions on Information Forensics and Security, v. 16, p. 4419-4434, 2021. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.