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Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization
In the second stage, we propose a simple and effective method for evaluating the confidence of pseudo-labels based on classification discrimination, and by learning consistency from high-quality pseudo-labels, we further refine the localization network to get better localization performance.
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