用近似物体尺寸代替精确标注，也能实现高精度分割。
Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation
- 用平均尺寸分布替代精确标签，配合简单损失函数即可
- 在PASCAL VOC上达到与像素级监督相当的分割精度
- 对标注误差鲁棒，适合缺乏精细标注的场景
本文揭示了一个令人意外的结果：在图像级监督下，将二元类别标签扩展为近似物体尺寸分布，即可使现成网络解决分割问题。采用简单的非零避免KL散度损失来优化平均预测，其分割精度可媲美使用完整真值掩码的标准像素级监督。相比之下，现有基于类别标签的方法通常需要复杂的不可复现结构修改和专用多阶段训练流程。我们在PASCAL VOC上使用新的人工标注的近似物体尺寸进行验证；还在COCO和医学数据上通过合成损坏的尺寸标签测试，结果表明所有标准网络对尺寸标注误差均表现出强鲁棒性。某些类别上，该方法的验证精度甚至优于像素级监督——后者对掩码错误不鲁棒。本工作为图像级监督下的分割提供了新思路，可能推动更通用的简化解决方案。
原文摘要 · Abstract (English)
This paper demonstrates a surprising result for segmentation with image-level targets: extending binary class tags to approximate relative object-size distributions allows off-the-shelf architectures to solve the segmentation problem. A straightforward zero-avoiding KL-divergence loss for average predictions produces segmentation accuracy comparable to the standard pixel-precise supervision with full ground truth masks. In contrast, current results based on class tags typically require complex non-reproducible architectural modifications and specialized multi-stage training procedures. Our ideas are validated on PASCAL VOC using our new human annotations of approximate object sizes. We also show the results on COCO and medical data using synthetically corrupted size targets. All standard networks demonstrate robustness to the size targets' errors. For some classes, the validation accuracy is significantly better than the pixel-level supervision; the latter is not robust to errors in the masks. Our work provides new ideas and insights on image-level supervision in segmentation and may encourage other simple general solutions to the problem.
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