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Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization
[article]
2019
arXiv
pre-print
In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations. The proposed approach is built on the observation that the proposal set from the training dataset is a collection of background, object parts, and objects. Several strategies are taken to adaptively eliminate the noisy proposals and generate pseudo object-level annotations for the weakly labeled dataset. A multiple instance
arXiv:1910.02101v2
fatcat:yeaqlgzpavhvlet5iqby33bmtu