# Self-Erasing Network for Integral Object Attention

Source: [Self-Erasing Network for Integral Object Attention](<https://www.sota2.com/research/paper/68fa64f61e209131cea075fe>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:14:52.998Z

Authors: Qibin Hou, Peng-Tao Jiang, Yunchao Wei, Ming-Ming Cheng

Published: 2018-10-23T12:53:56.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Weakly Supervised Semantic Segmentation on PASCAL VOC 2012 (val)](<https://www.sota2.com/research/sota/weakly-supervised-semantic-segmentation-on-pascal-voc-2012-val.md>): Weakly supervised semantic segmentation; PASCAL VOC 2012 (val); mIoU: 63.1.

- [Weakly Supervised Semantic Segmentation on PASCAL VOC 2012 (test)](<https://www.sota2.com/research/sota/weakly-supervised-semantic-segmentation-on-pascal-voc-2012-test.md>): Weakly supervised semantic segmentation; PASCAL VOC 2012 (test); mIoU: 62.8.

- [Semantic Segmentation on PASCAL VOC 2012 (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-pascal-voc-2012-test.md>): Semantic segmentation; PASCAL VOC 2012 (test); mIoU: 62.8.

- [Semantic Segmentation on PASCAL VOC 2012 (val)](<https://www.sota2.com/research/sota/semantic-segmentation-on-pascal-voc-2012-val.md>): Semantic segmentation; PASCAL VOC 2012 (val); Mean IoU: 63.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1810.09821>)

[PDF](<https://arxiv.org/pdf/1810.09821.pdf>)