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Visual attention models based on compressed domain video analysis techniques

Grant number: 21/02739-0
Support type:Scholarships in Brazil - Master
Effective date (Start): April 01, 2021
Effective date (End): January 31, 2023
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Cooperation agreement: Microsoft Research
Principal researcher:João Paulo Papa
Grantee:Pedro Lamkowski dos Santos
Home Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , 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


Visual attention models take inspiration from how the human eye works, i.e., we can acquire a broad view of the scene we are looking at, but we tend to focus on a particular region only. Usually, such a region is the one that, for some reason, calls our attention to it. When we work with compressed images, e.g., JPG, PNG, and GIF, we first need to decode them before feeding Convolutional Neural Networks (CNNs) for proper feature extraction. Indeed, this operation demands additional computational costs. Therefore, the design of CNNs capable of learning directly from the compressed domain is highly desirable. In this proposal, we aim at combining attention models and deep networks to work on the compressed domain, whose outcomes will be evaluated in the context of weak video classification. We expect that the proposed models will turn into more efficient approaches. (AU)

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