title: Instance Annotation via Optimal BoW for Weakly Supervised Object Localization.
abstract: In this paper, we aim at irregular-shape object localization under weak supervision. With over-segmentation, this task can be transformed into multiple-instance context. However, most multiple-instance learning methods only emphasize single most positive instance in a positive bag to optimize bag-level classification, and leads to imprecise or incomplete localization. To address this issue, we propose a scheme for instance annotation, where all of the positive instances are detected by labeling each instance in each positive bag. Inspired by the successful application of bag-of-words (BoW) to feature representation, we leverage it at instance-level to model the distributions of the positive class and negative class, and then incorporate the BoW learning and instance labeling in a single optimization formulation. We also demonstrate that the scheme is well suited to weakly supervised object localization of irregular-shape. Experimental results validate the effectiveness both for the problem of generic instance annotation and for the application of weakly supervised object localization compared to some existing methods.
authors.forename: Liantao
authors.initials: L
authors.lastname: Wang
authors.forename: Deyu
authors.initials: D
authors.lastname: Meng
authors.forename: Xuelei
authors.initials: X
authors.lastname: Hu
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Lu
authors.forename: Ji
authors.initials: J
authors.lastname: Zhao