| Grant number: | 19/02808-1 |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate (Direct) |
| Start date: | July 17, 2019 |
| End date: | July 16, 2020 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Moacir Antonelli Ponti |
| Grantee: | Leo Sampaio Ferraz Ribeiro |
| Supervisor: | John Collomosse |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Institution abroad: | University of Surrey, England |
| Associated to the scholarship: | 17/22366-8 - Generative networks and feature learning for cross domain visual search, BP.DD |
Abstract 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 variants of Generative Adversarial Networks and how network weights of autoencoder models can be used as a concrete representation for the domain gap. We expect that both paths of study should lead to the development of novel methods for unsupervised feature matching and state-of-the-art cross-domain image retrieval. (AU) | |
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