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Neural Architecture Search for Enhancing Action Video Recognition in Compressed Domains

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Author(s):
Lamkowski, Pedro ; Rodrigues, Douglas ; Passos, Leandro A. ; Papa, Joao P. ; Almeida, Jurandy
Total Authors: 5
Document type: Journal article
Source: 2024 31ST INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING, IWSSIP 2024; v. N/A, p. 7-pg., 2024-01-01.
Abstract

Video classification models have become one of the most widely used topics in the computer vision field, encompassing many tasks such as medical, security, industrial, and other applications. Although deep learning models have achieved great results in the video domain, such models are built to operate in the domain of RGB frame sequences. In such models, a prior step is required for decoding video data since the vast majority relies on compressed formats. Nevertheless, large amounts of computational resources are required for decoding, especially in real-time. Researchers have already tackled the task of building networks that work in the compressed domain with promising results but with architectures still very close to those used for the RGB domain. We propose an approach that employs Neural Architecture Search to explore and find the most effective architectures for the compressed domain. Our approach was tested on UCF101 and HMDB51 datasets, obtaining a computationally less complex architecture than similar methods. (AU)

FAPESP's process: 23/03726-4 - On the Study and Development of Multi-method Multi-objective Algorithms
Grantee:Douglas Rodrigues
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
Grantee:Fabio Gagliardi Cozman
Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 23/14427-8 - Data Science for Smart Industry (CDII)
Grantee:José Alberto Cuminato
Support Opportunities: Research Grants - Research Centers in Engineering Program