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Self-Erasing Network for Integral Object Attention
Qibin Hou · PengTao Jiang · Yunchao Wei · Ming-Ming Cheng

Tue Dec 04 02:00 PM -- 04:00 PM (PST) @ Room 210 #69

Recently, adversarial erasing for weakly-supervised object attention has been deeply studied due to its capability in localizing integral object regions. However, such a strategy raises one key problem that attention regions will gradually expand to non-object regions as training iterations continue, which significantly decreases the quality of the produced attention maps. To tackle such an issue as well as promote the quality of object attention, we introduce a simple yet effective Self-Erasing Network (SeeNet) to prohibit attentions from spreading to unexpected background regions. In particular, SeeNet leverages two self-erasing strategies to encourage networks to use reliable object and background cues for learning to attention. In this way, integral object regions can be effectively highlighted without including much more background regions. To test the quality of the generated attention maps, we employ the mined object regions as heuristic cues for learning semantic segmentation models. Experiments on Pascal VOC well demonstrate the superiority of our SeeNet over other state-of-the-art methods.

Author Information

Qibin Hou (Nankai University)
PengTao Jiang (Nankai University)
Yunchao Wei (UIUC)
Ming-Ming Cheng (Nankai University)

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