MetaMIML: Meta Multi-Instance Multi-Label Learning

no code implementations • 7 Nov 2021 • Yuanlin Yang, Guoxian Yu, Jun Wang, Lei Liu, Carlotta Domeniconi, Maozu Guo

Multi-Instance Multi-Label learning (MIML) models complex objects (bags), each of which is associated with a set of interrelated labels and composed with a set of instances.

Meta-Learning Multi-Label Learning +1

Multi-typed Objects Multi-view Multi-instance Multi-label Learning

no code implementations • 6 Oct 2020 • Yuanlin Yang, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang

Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels.

Multi-Label Learning