Keynote Speakers
Prof. Yiu-Ming Cheung (FIEEE, FAAAS, FIET, FBCS)
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Speech Title: Imbalanced Data Learning: From Class Imbalance to Long-tailed Data Classification for Visual Recognition
Abstract: Imbalance data refer to the number of samples among classes is extremely imbalanced, which is common in our daily life, e.g. medical diagnosis, and autonomous driving. In general, the problem of learning from imbalanced data is nontrivial and challenging in the field of data engineering and machine learning, which has attracted growing attentions in recent years. In this talk, the imbalance data learning problem is introduced from class imbalance to long-tailed data learning, including their potential applications, and the impacts from a model learning perspective. Then, the latest research progress on imbalance data learning will be reviewed, including some representative methods in the literature. Lastly, the potential research directions in this field will be discussed.
About
ICIVC is an ideal platform to exchange insights and discuss the development of image, vision and computing. It has been held in Porthmouth, UK; Chengdu, Dalian, Xi'an, China, etc...
