supported by the National Natural Science Foundation of China(No.61876121,No.61472267,No.61728205,No.61502329,No.61672371);Primary Research&Developement Plan of Jiangsu Province(No.BE2017663);Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(No.19KJB520054);Foundation of Key Laboratory in Science and Technology Development Project of Suzhou(No.SZS201609,No.SZS201813)
Great efforts have been made by using deep neural networks to recognize multi-label images.Since multi-label image classification is very complicated,many studies seek to use the attention mechanism as a kind of guida...
supported by National Natural Science Foundation of China(No.61272222,No.61003116);Natural Science Foundation of Jiangsu Province of China(No.BK2011782);Key(Major)Program of Natural Science Foundation of Jiangsu Province of China(No.BK2011005)
Multi-label learning deals with each instance which may be associated with a set of class labels simultaneously. We propose a novel multi-label classification approach named MFSM(Multi-task joint feature selection for...
Automatic image annotation has emerged as an important research topic. From the perspective of machine learning, the annotation task fits both multiinstance and multi-label learning framework due to the fact that an i...