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of 6
pro vyhledávání: '"Jaradat, Shadi"'
Autor:
Elhenawy, Mohammed, Abutahoun, Ahmad, Alhadidi, Taqwa I., Jaber, Ahmed, Ashqar, Huthaifa I., Jaradat, Shadi, Abdelhay, Ahmed, Glaser, Sebastien, Rakotonirainy, Andry
Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning text, images, and audio to adeptly tackle complex problems, including zero-shot in-context learning scenarios. This study explores the ability of MLLMs in visually solv
Externí odkaz:
http://arxiv.org/abs/2407.00092
Object detection is a critical component of transportation systems, particularly for applications such as autonomous driving, traffic monitoring, and infrastructure maintenance. Traditional object detection methods often struggle with limited data an
Externí odkaz:
http://arxiv.org/abs/2406.10712
This study explores traffic crash narratives in an attempt to inform and enhance effective traffic safety policies using text-mining analytics. Text mining techniques are employed to unravel key themes and trends within the narratives, aiming to prov
Externí odkaz:
http://arxiv.org/abs/2406.09438
Autor:
Elhenawy, Mohammed, Abdelhay, Ahmed, Alhadidi, Taqwa I., Ashqar, Huthaifa I, Jaradat, Shadi, Jaber, Ahmed, Glaser, Sebastien, Rakotonirainy, Andry
Multimodal Large Language Models (MLLMs) have demonstrated proficiency in processing di-verse modalities, including text, images, and audio. These models leverage extensive pre-existing knowledge, enabling them to address complex problems with minima
Externí odkaz:
http://arxiv.org/abs/2406.06865
Publikováno v:
In Transportation Engineering September 2024 17
Autor:
Jaradat, Shadi1,2 (AUTHOR) r.nayak@qut.edu.au, Nayak, Richi2,3 (AUTHOR), Paz, Alexander4 (AUTHOR) alexander.paz@qut.edu.au, Elhenawy, Mohammed1 (AUTHOR) shadi.jaradat@hdr.qut.edu.au
Publikováno v:
Algorithms. Jul2024, Vol. 17 Issue 7, p284. 23p.