A comparative analysis of depth feature for multimedia recognition tasks

• Grant number: 22/01246-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: March 01, 2022
• End date: January 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: Microsoft Research
• Principal Investigator: João Paulo Papa
• Grantee: Arissa Yoshida
• Host 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

Abstract

Multimedia recognition tasks aim to identify which kind of category a particular video belongs to, i.e., it can be either a romantic or sci-fi movie. The video may also present specific actions, such as walking in the park, sports, and a race. This project's primary goal is to compare many deep architectures for feature learning and video classification to establish whether a particular type of deep network is more appropriate to video classification. We will investigate different types of neural architectures on public video datasets. (AU)