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Learning to Localize Objects with Noisy Labeled Instances

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Figure

Figure 1: Given a collection of noisy labeled instances, ourgoal is to automatically discover those well localized in-stances (a) and propagate such priors to those noisy labeledinstances (b) for better localization.
Figure 2: An overview of SD-LocNet. It starts from noisy labeled instances whose object bounding boxes (latent variables) arenot accurate, and updates the latent variables with reliable instances discovered by adaptive sampling
Table 1: Effects of various design modules for Localization.
Figure 3: Localization results on PASCAL VOC 2007 versus (a) number of clusters K, (b) start updating epoch T0, and (c)number of candidate positives Rp
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