CV Lattes
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Universidade Estadual de Campinas (UNICAMP). Instituto de Computação (IC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
M.Sc. student in Computer Science (09/2020-present) at the Computer Science Post-Graduation Program (PPG-CC) of Institute of Science and Technology of Universidade Federal de São Paulo (ICT-UNIFESP), with a research internship carried out with the MHUG group (Multimedia and Human Understanding Group) of the Dipartimento di Ingegneria e Scienza dell'Informazione, Università Degli Studi di Trento in Trento, Italy (02/2022-07/2022). He received a B.Sc. degree in Science and Technology (2019) and in Computer Engineering (2021) also from ICT-UNIEFSP. During his B.Sc. he carried out undergraduated researches funded by CNPq (09/2017-06/2019) and FAPESP (06/2019-12/2019). He has developed research on Semantic Information Retrieval in Multimedia Data, mainly, about Domain Adaptation in Deep Learning methods. (Source: Lattes Curriculum)
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Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), have brought revolutionary advances to many research areas due to their capacity to learn from raw data. However, such astonishing results were obtained at the cost of big and expensive models that run on top of unrealistic computational systems. That kind of huge models, like Stable Diffusion and GPT-3, are norm…
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 flexibil…
Digital videos have become the medium of choice for a growing number of people communicating via Internet and their mobile devices. Over the past decade, world has witnessed an explosive growth in the amount of video data fostered by astonishing technological developments. In this scenario, there is a growing demand for efficient systems to reduce the work and information overload for peo…
Deep learning (DL) methods have brought revolutionary advances to many research areas due to their capacity of learning from raw data. Particularly, convolutional neural networks (CNNs) have substantially increased the development of intelligent multimedia systems, like natural language processing and computer vision systems. However, when those methods are applied to non-controllable env…
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