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Object attention maps generated by image classifiers are usually used as priors for weakly-supervised semantic segmentation. However, attention maps usually locate the most discriminative object parts. The lack of integral object localization maps heavily limits the performance of weakly-supervised segmentation approaches. This paper attempts to investigate a novel way to identify entire object regions in a weakly-supervised manner. We observe that image classifiers' attention maps at different training phases may focus on different part
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