Objectives

• Develop state-of-the-art algorithms and models: Focus on core computer vision tasks such as image recognition, object detection, segmentation, 3D reconstruction, motion analysis, and scene understanding.
• Explore novel approaches: Investigate emerging areas like deep learning, generative models, and explainable AI to address challenging problems and unlock new possibilities.
• Bridge the gap between theory and practice: Translate research findings into robust and efficient software tools, datasets, and benchmarks that benefit the wider research community and industry.
• Foster interdisciplinary collaboration: Partner with researchers from related fields like robotics and natural language processing to tackle complex real-world challenges.

• Train the next generation of computer vision experts: Mentor and educate students through research projects, courses, and workshops, equipping them with the skills and knowledge to excel in academia and industry.
• Promote knowledge sharing: Organize conferences, seminars, and outreach events to disseminate research findings and foster a vibrant community of computer vision researchers and practitioners.

• Contribute to societal well-being: Apply computer vision technologies to address critical societal challenges in areas like healthcare, environmental sustainability, accessibility, and security.
• Engage with industry: Collaborate with companies to translate research into practical applications, driving innovation and economic growth
• Promote ethical considerations: Develop and advocate for responsible and ethical practices in computer vision research and deployment, ensuring fairness, transparency, and accountability.