Our Research

Current Projects

Dense Labeling of Remote Sensing Images in the Wild

Dense Labeling of Remote Sensing Images in the Wild

This project address the challenges for the effective use of supervised learning in dense pixel ...
Pattern recognition using small annotated data sets

Pattern recognition using small annotated data sets

This project proposes the development of new approaches to deal with pattern recognition in ...
Deep Representations for Large-Scale Geo-Mapping

Deep Representations for Large-Scale Geo-Mapping

This project aims to address the problem of pattern recognition for the creation of thematic maps ...

Past Projects

AI Methodologies For  Environmental Monitoring

AI Methodologies For Environmental Monitoring

This research project aims to develop methodologies in the selection process of audit objects ...
Diagnosis Aid Using Chest X-Ray and Deep Learning

Diagnosis Aid Using Chest X-Ray and Deep Learning

The CADCOVID-19 project aims to develop an online system to assist in the diagnosis of COVID-19 ...
Automatic Thematic Maps Using Multiple Sensors

Automatic Thematic Maps Using Multiple Sensors

Geographic Information Systems (GIS) are automated systems whose purpose is to store, analyze and ...
Artificial Intelligence Systems for External Control

Artificial Intelligence Systems for External Control

As políticas de proteção ambiental e climática requerem complexas estruturas de governança ...

Partners

Our team has conducted/participated in various consulting and R&D activities with companies and important public institutions. We have also wide experience in Continuing Education Programs (extension school) for industry professionals.

News

Pesquisa feita no DCC/UFMG, em parceria com a Polícia Federal e a UNICAMP, agiliza a identificação de materiais de violência sexual infantil

Saiba mais: https://bit.ly/3PsWrjT
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#dccufmg #patreo #processamentodeimagens #unicamp
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🔗 https://linktr.ee/dccufmg

Conheça um pouco mais sobre o nosso laboratório:

https://www.instagram.com/reel/CdGA6EuDcGY/?utm_source=ig_web_button_share_sheet

Semantically segmenting images using SOT techniques
often require large amounts of labeled data, which is laborious to produce.
Our recent work leverages meta-learning to segment using few-shot examples of sparsely labeled data, which are easier to acquire
https://ieeexplore.ieee.org/document/9744585

Oportunidade de Pós-doutorado em Applied Machine Learning - Projeto SIMOA, UFMG/CEMIG

§ Valor da bolsa:
- Pós doutorado: R$ 5.200,00

Inscrições e outros detalhes: https://lnkd.in/dWsPJQi2

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