Learning and Vision Lab, ECE, NUS

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Learning and Vision Group at the National University of Singapore was founded by Associate Professor Shuicheng Yan in December 2007, and is currently led by Assistant Professor Jiashi Feng ever since its founder started his ambitious journey in the industrial community from 2015.

Since its birth, the LV group has been concentrating on going beyond known boundaries and encouraging most original research and technologies at the edges for computer vision, machine learning and their applications in real life. The research projects of LV range from fundamental machine learning (including deep learning) methods, to the cutting-edge face/human and image/video analytics techniques, to intelligent search and recommendation systems.

The past decade has witnessed the astonishing growth of the LV group and its great achievements. Till now, LV-ers have published more than 600 technical papers in top international academic journals and conferences, among which over 10 were granted best paper or best student paper prizes; they received winner or honorable-mention prizes for over 10 times at core competitions of computer vision, i.e. Pascal VOC and ImageNet; and they obtained 3 industrial licenses, which well demonstrate the great commercial potential of their research besides the academic significance. In particular, this excellent team managed to complete a grand slam in ACM MM, the top conference in multimedia, including Best Paper Award, Best Student Paper Award and Best Demo Award.

Currently the LV group has 16 PhD students, 7 Research Fellows and Research Assistants, as well as a number of visiting professors and students (The statistics may vary with time). Till August 2019, over 100 alumni had worked in LV, including 21 PhD students, 43 Research Fellows/Assistants and over 30 visiting professors and students, and are now holding positions in overseas universities/companies and continue contributing to the community and society.

The LV group is committed to looking beyond the obvious to target at the problems not yet investigated or not yet solved problems whose solutions could radically improve the computer vision and machine learning technologies as well as improve the way people live.