CV Lattes
Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Acting on the following major areas of interest: Sketch-based Image Retrieval, Content-based Image Retrieval, Image Processing, Machine Learning, Data Compression and Deep Learning. (Source: Lattes Curriculum)
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Feature learning methods have reached state of the art on many areas of study. Despite excellent results achieved on benchmark datasets, there is still little understanding of its inner workings, and applications to be explored, particularly when considering architectures that go beyond traditional convolutional neural networks. In this project, we propose the use of feature learning for …
Siamese and triplet-loss-based nets, both variations of Convolutional Neural Networks (CNNs), are commonly applied to Content-based Image Retrieval and Domain Mapping systems thanks to their natural characteristic of generating feature spaces tuned for retrieval. There is however an improvement opportunity lying on the representation of the generated data; focusing on limited resources pl…
There are image classification problems in which each image are represented by regions of interest; for each region a series of features are extracted. As a result a set of feature vectors are available, and it is necessary to assign a label to this set of instances. The Multi-Instance Learning studies the problem in which each object is describes as a bag (set of instances). This project…
Current advances within cross-domain image retrieval are mostly dependent on the availability of large amounts of annotated data. In this project we seek to explore the use and development of unsupervised feature learning methods that can overcome the natural domain gap when dealing with images from different styles and domains. We are particularly interested in representations infered by…
There are image classification problems in which each image is represented by regions of interest; for each region a series of features can be extracted. As a result a set of feature vectors are available, and it is necessary to assign a label to this set of instances. The Multiple-Instance Learning (MIL) studies the problem in which each object is described as a bag (set of instances). T…
| 3 / 3 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 5 / 5 | All Scholarships |
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