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Open set methods based on deep networks for multimedia recognition

Grant number: 20/08770-3
Support type:Scholarships in Brazil - Master
Effective date (Start): September 01, 2020
Effective date (End): August 31, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Cooperation agreement: Microsoft Research
Principal researcher:Jurandy Gomes de Almeida Junior
Grantee:Lucas Fernando Alvarenga e Silva
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , 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

Nowadays the use of computational vision methods on real-world problems is growing mainly by the achievement of better results on image and video recognition tasks. These results have been achieved by the evolution of machine learning methods, specifically by deep neural networks. However, these methods are usually adapted for tasks where their limits are well known, and have low flexibility for data that have been out of the problem scope. In real-world problems, we expect that classification methods should achieve equivalent results of the training step. But when used on these problems, we can see low classification performance. This happens by the fact that deep networks do not generalize well on unknown data or domain different data, which is known as category shift and domain shift problems. In front of this problem, researchers have been developing methods to avoid this gap with unknown data on two main research lines: Unsupervised domain adaptation methods and Open-set methods, also known by open-world methods. While domain adaptation methods try to align domain distributions, open-set methods should predict if some data can be recognizable by the network. The goal of this work is to study and investigate open-set methods and develop new state-of-art approaches on images and video recognition tasks. (AU)

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)
ROCHA, GUILHERME CONCEICAO; PAIVA, HENRIQUE MOHALLEM; SANCHES, DAVI GONCALVES; FIKS, DANIEL; CASTRO, RAFAEL MARTINS; ALVARENGA E SILVA, LUCAS FERNANDO. Information system for epidemic control: a computational solution addressing successful experiences and main challenges. LIBRARY HI TECH, MAR 2021. Web of Science Citations: 0.

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