CRCV Brochure The common goal and purpose of the center is to strongly promote basic research in computer vision and its applications in all related areas including National Defense & Intelligence, Homeland Security, Environment Monitoring, Life Sciences and Biotechnology and Robotics. The Mission of CRCV is to: Be the world class leader in research, commercialization, scholarship and education in computer vision Conduct fundamental and applied research upon which future Computer Vision industries can be built. Provide highest quality education and research experience in computer vision at all levels from K-12 to undergraduate to doctoral and postdoctoral levels to develop a highly skilled workforce for Florida’s economic development, More News Four Papers Accepted in AAAI 2023 November 19, 2022 Read More Three Papers Accepted in NeurIPS 2022 November 9, 2022 Read More UCF Computer Vision Team Receives Prestigious Computer Vision Award for Action Recognition Dataset October 28, 2022 Read More Omar Javed ’05 PhD, VP of Applied Science at Twitch was Honored with UCF Computer Science Distinguished Alumni Award October 14, 2022 Read More More News US Patent titled “Methods of real-time spatio-temporal activity detection and categorization from untrimmed video segments” October 11, 2022 Read More UCF-led Research Team to Play Key Role in National $26M NSF-funded Effort to Develop Smart Streetscapes August 19, 2022 Read More CRCV Solicits Applications for Assistant Professor or Associate Professor, Deep Learning and Computer Vision August 31, 2022 Read More UCF CRCV is core Partner for $26 Million NSF ERC on Smart Streetscapes Led by Columbia University August 9, 2022 Read More Events Archived Events Let’s get social Research Highlights Persistent research endeavors by individuals associated to CRCV continue to stir new ideas and technological advances in the domain. We also strive towards disseminating knowledge thorough seminars, presentations and publications. Large-scale Image Geo-Localization Using Dominant Sets Unsupervised Meta-Learning for Few-Shot Image Classification On Symbiosis of Attribute Prediction and Semantic Segmentation
