Multiple-Instance learning from multiple perspectives: Combining models for Multiple-Instance learning

摘要

Multiple-Instance learning (MIL), which relaxes training annotation granularity from instance level to instance collection (bag) level by applying bag concept, obtains increasing attentions from computer vision community. Due to its flexible annotation mechanism, MIL has been naturally utilized on a variety of computer vision problems. And numerous models have been proposed, each of which is ingeniously designed to catch certain characteristics of MIL. However different models only perform well on certain tasks, and further improvement can hardly be achieved.