Self-Erasing Network for Integral Object Attention

Understand

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.