Zobrazeno 1 - 10
of 1 234 215
pro vyhledávání: '"MOHAMED, A. A."'
Autor:
Mohamed, Youssef, Li, Runjia, Ahmad, Ibrahim Said, Haydarov, Kilichbek, Torr, Philip, Church, Kenneth Ward, Elhoseiny, Mohamed
Research in vision and language has made considerable progress thanks to benchmarks such as COCO. COCO captions focused on unambiguous facts in English; ArtEmis introduced subjective emotions and ArtELingo introduced some multilinguality (Chinese and
Externí odkaz:
http://arxiv.org/abs/2411.03769
Autor:
Abo-eleneen, Amr, Helmy, Menna, Abdellatif, Alaa Awad, Erbad, Aiman, Mohamed, Amr, Abdallah, Mohamed
In the face of increasing demand for zero-touch networks to automate network management and operations, two pivotal concepts have emerged: "Learn to Slice" (L2S) and "Slice to Learn" (S2L). L2S involves leveraging Artificial intelligence (AI) techniq
Externí odkaz:
http://arxiv.org/abs/2411.03686
Autor:
Saeed, Muhammed, Mohamed, Elgizouli, Mohamed, Mukhtar, Raza, Shaina, Shehata, Shady, Abdul-Mageed, Muhammad
Large language models (LLMs) are widely used but raise ethical concerns due to embedded social biases. This study examines LLM biases against Arabs versus Westerners across eight domains, including women's rights, terrorism, and anti-Semitism and ass
Externí odkaz:
http://arxiv.org/abs/2410.24049
Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pr
Externí odkaz:
http://arxiv.org/abs/2410.15602
Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remain
Externí odkaz:
http://arxiv.org/abs/2410.06935
Autor:
Talafha, Bashar, Kadaoui, Karima, Magdy, Samar Mohamed, Habiboullah, Mariem, Chafei, Chafei Mohamed, El-Shangiti, Ahmed Oumar, Zayed, Hiba, tourad, Mohamedou cheikh, Alhamouri, Rahaf, Assi, Rwaa, Alraeesi, Aisha, Mohamed, Hour, Alwajih, Fakhraddin, Mohamed, Abdelrahman, Mekki, Abdellah El, Nagoudi, El Moatez Billah, Saadia, Benelhadj Djelloul Mama, Alsayadi, Hamzah A., Al-Dhabyani, Walid, Shatnawi, Sara, Ech-Chammakhy, Yasir, Makouar, Amal, Berrachedi, Yousra, Jarrar, Mustafa, Shehata, Shady, Berrada, Ismail, Abdul-Mageed, Muhammad
In spite of the recent progress in speech processing, the majority of world languages and dialects remain uncovered. This situation only furthers an already wide technological divide, thereby hindering technological and socioeconomic inclusion. This
Externí odkaz:
http://arxiv.org/abs/2410.04527
Autor:
Elnoor, Mohamed, Weerakoon, Kasun, Seneviratne, Gershom, Xian, Ruiqi, Guan, Tianrui, Jaffar, Mohamed Khalid M, Rajagopal, Vignesh, Manocha, Dinesh
We present a novel autonomous robot navigation algorithm for outdoor environments that is capable of handling diverse terrain traversability conditions. Our approach, VLM-GroNav, uses vision-language models (VLMs) and integrates them with physical gr
Externí odkaz:
http://arxiv.org/abs/2409.20445
Autor:
Seneviratne, Gershom, Weerakoon, Kasun, Elnoor, Mohamed, Rajgopal, Vignesh, Varatharajan, Harshavarthan, Jaffar, Mohamed Khalid M, Pusey, Jason, Manocha, Dinesh
We present CROSS-GAiT, a novel algorithm for quadruped robots that uses Cross Attention to fuse terrain representations derived from visual and time-series inputs, including linear accelerations, angular velocities, and joint efforts. These fused rep
Externí odkaz:
http://arxiv.org/abs/2409.17262
Autor:
Weerakoon, Kasun, Elnoor, Mohamed, Seneviratne, Gershom, Rajagopal, Vignesh, Arul, Senthil Hariharan, Liang, Jing, Jaffar, Mohamed Khalid M, Manocha, Dinesh
We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes guided by human instructions and leveraging Vision Language Models (VLMs). Our method interprets human commands using a Large Language Model (LLM) and categorizes th
Externí odkaz:
http://arxiv.org/abs/2409.16484
Deep learning (DL) models are popular across various domains due to their remarkable performance and efficiency. However, their effectiveness relies heavily on large amounts of labeled data, which are often time-consuming and labor-intensive to gener
Externí odkaz:
http://arxiv.org/abs/2411.05752