The goal of traditional multi-instance learning (MIL) is to predict the labels of the bags, whereas in many real applications, it is desirable to get the instance labels, especially the labels of key instances that trigger the bag labels, in addition to getting bag labels. Such a problem has been largely unexplored before. In this paper, we formulate the Key Instance Detection (KID) problem, an...

Multi-instance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification. This thesis investigates the case where each instance has a weight value determining the level of influ...

Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, the...

Wei Li1 [email protected] Changhu Wang2 [email protected] Lei Zhang3 [email protected] Yong Rui2 [email protected] Bo Zhang1 [email protected] 1 State Key Lab of Intelligent Technology and Systems, TNList, Department of Computer Science and Technology, Tsinghua University Beijing 100084, China 2 Microsoft Research No. 5 Danling Street, Haidian District, Beijing 100080, China...

Multi-instance learning (MIL) deals with the tasks where each example is represented by a bag of instances. A bag is positive if it contains at least one positive instance, and negative otherwise. The positive instances are also called key instances. Only bag labels are observed, whereas specific instance labels are not available in MIL. Previous studies typically assume that training and test ...

In this study, an improved end-to-end framework for instance segmentation of military camouflaged targets, referred to as MilInst, is proposed. The builds upon SparseInst method developed by Cheng et al . [27]. Several improvements are introduced enhance the model’s performance. First, Receptiv...