arXiv ·
Researchers in Saudi Arabia are applying computer vision techniques to reduce Camel-Vehicle Collisions (CVCs). They tested object detection models including CenterNet, EfficientDet, Faster R-CNN, SSD, and YOLOv8 on the task, finding YOLOv8 to be the most accurate and efficient. Future work will focus on developing a system to improve road safety in rural areas.
arXiv ·
Researchers are exploring computer vision models to mitigate Camel-Vehicle Collisions (CVC) in Saudi Arabia, which have a high fatality rate. They tested CenterNet, EfficientDet, Faster R-CNN, and SSD for camel detection, finding CenterNet to be the most accurate and efficient. Future work involves developing a comprehensive system to enhance road safety in rural areas.
KAUST ·
KAUST researchers developed a low-cost, AI-powered drone system to recognize and track camels, addressing challenges faced by local herders. The system uses commercial drones, cameras, and machine learning to monitor camel herds in real time without expensive GPS collars. The AI model revealed insights into camel migration patterns, showing coordinated grazing and sensitivity to drone sounds. Why it matters: This system offers an affordable solution to preserve Saudi Arabia's camel herding tradition while providing valuable insights into camel behavior and contributing to the local economy.
arXiv ·
The paper introduces Juhaina, a 9.24B parameter Arabic-English bilingual LLM trained with an 8,192 token context window. It identifies limitations in the Open Arabic LLM Leaderboard (OALL) and proposes a new benchmark, CamelEval, for more comprehensive evaluation. Juhaina outperforms models like Llama and Gemma in generating helpful Arabic responses and understanding cultural nuances. Why it matters: This culturally-aligned LLM and associated benchmark could significantly advance Arabic NLP and democratize AI access for Arabic speakers.
KAUST ·
KAUST President Jean-Lou Chameau participated in a panel discussion on academic excellence at The 2015 Economist Higher Education Forum in Manhattan. He highlighted KAUST's design and role as a global science and technology university. The forum, which hosted leaders in higher education, included an online conversation using #HigherEdForum. Why it matters: While dated, the event underscores KAUST's ongoing efforts to engage with global thought leaders and promote its vision for graduate education, research, and entrepreneurship.
arXiv ·
The paper introduces a framework for camel farm monitoring using a combination of automated annotation and fine-tune distillation. The Unified Auto-Annotation framework uses GroundingDINO and SAM to automatically annotate surveillance video data. The Fine-Tune Distillation framework then fine-tunes student models like YOLOv8, transferring knowledge from a larger teacher model, using data from Al-Marmoom Camel Farm in Dubai.
KAUST ·
KAUST President Jean-Lou Chameau spoke at the Times Higher Education MENA Universities Summit in Doha, Qatar. He shared his experiences from Caltech and Georgia Tech, emphasizing KAUST's historic undertaking. KAUST's research output leads Saudi Arabia and surpassed other Arab institutes in 2014 according to the Nature Index report. Why it matters: The summit and KAUST's participation highlight the increasing role of universities in driving economic diversification and knowledge creation in the MENA region.
KAUST ·
KAUST President Jean-Lou Chameau announced his retirement, effective at the end of August 2017, after more than 25 years in academic leadership positions. He will serve on the Presidential Search Committee to identify his successor. Chameau expressed pride in the KAUST community and its accomplishments. Why it matters: The transition marks a significant leadership change for KAUST as it continues to develop as a leading science and technology university in the region.