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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