KAUST computer scientist Mohamed Elhoseiny and his VISION CAIR team developed Creative Walk Adversarial Networks (CWAN) for novel art generation. CWAN learns from existing art styles and deviates using 'random walk deviation' methods. Human evaluators preferred CWAN-generated art compared to other methods like StyleGAN2. Why it matters: The research demonstrates AI's potential as a valuable tool for artists, enabling the creation of unique and meaningful art, and explores more effective emotional language in image captioning.
Dr. James She from Hamad Bin Khalifa University will give a talk on AI for Art, Culture and Heritage, covering history, trends, opportunities, and potential impacts in the Gulf region. His research focuses on AI and multimedia technologies for art, media, culture, and human creativity, with recent artworks using AI for cultural or heritage content in Arabic regions. He was also a visiting artist-in-residency at Fire Station Museum, Qatar Museums in 2020-2021. Why it matters: This lecture highlights the growing interest and applications of AI in preserving and promoting cultural heritage within the Gulf region, potentially fostering innovation in art and culture.
Researchers at MBZUAI have developed a new method for controllable poetry generation in Arabic and its dialects, moving beyond traditional analysis tasks for Arabic poetry within Large Language Models (LLMs). They introduce a large-scale, instruction-based dataset in Modern Standard Arabic (MSA) and various Arabic dialects, enabling LLMs to perform tasks like writing, revising, and continuing poems based on user criteria. Experiments show that fine-tuning LLMs on this dataset results in models capable of generating poetry aligned with user requirements, validated by automated metrics and human evaluation. Why it matters: This work represents a significant advancement in Arabic Natural Language Processing, offering tools for creative expression and cultural preservation while opening new avenues for user-guided content generation in culturally rich text forms.