Self-Erasing Network for Integral Object Attention
Visual results on a complex image with 4 semantic categories
Code and results on VOC images are available [here]
Per-category IoU scores are available:
VOC val set
| category | IoU (VGG) | IoU (ResNet-101) |
|---|---|---|
| background | 0.890 | 0.897 |
| aeroplane | 0.824 | 0.858 |
| bicycle | 0.324 | 0.437 |
| bird | 0.715 | 0.775 |
| boat | 0.634 | 0.617 |
| bottle | 0.627 | 0.681 |
| bus | 0.803 | 0.760 |
| car | 0.728 | 0.668 |
| cat | 0.799 | 0.832 |
| chair | 0.178 | 0.173 |
| cow | 0.692 | 0.790 |
| dining table | 0.194 | 0.100 |
| dog | 0.736 | 0.805 |
| horse | 0.718 | 0.811 |
| motorbike | 0.693 | 0.744 |
| person | 0.683 | 0.757 |
| potted plant | 0.355 | 0.389 |
| sheep | 0.730 | 0.774 |
| sofa | 0.266 | 0.196 |
| train | 0.735 | 0.676 |
| tv/monitor | 0.502 | 0.507 |
| mean | 0.611 | 0.631 |
If you think this work is helpful for you, please cite:
@inproceedings{hou2018selferasing,
title={Self-Erasing Network for Integral Object Attention},
author={Hou, Qibin and Jiang, Peng-Tao and Wei, Yunchao and Cheng, Ming-Ming},
booktitle={NeurIPS},
year={2018}
}
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