.png){kind=icon}
Return
Instance Annotation via Optimal BoW for Weakly Supervised Object Localization
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2017.2647965.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Bag-of-words (BoWs)
instance annotation
object localization
weakly supervised learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSave
ShareSaveShareSaveShareSaveShareSave
View more