Segment objects by learning a stable hardness value for pixels with Hardness-Level-Learning
Segment objects by learning a stable hardness value for pixels with Hardness-Level-Learning
Not All Pixels Are Equal: Learning Pixel Hardness for Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2305.08462
arXiv PDF paper https://arxiv.org/pdf/2305.08462.pdf
Semantic segmentation ... in some hard areas (e.g., small objects or thin parts) is still not promising.
... existing hard pixel mining ... rely on ... loss value, which ... decrease during training ... Intuitively ... hardness ... depends on image structure and is expected to be stable.
... propose to learn pixel hardness for semantic segmentation, leveraging hardness information contained in global and historical loss values.
... add a gradient-independent branch for learning a hardness level (HL) map ... encourages large hardness values in difficult areas, leading to appropriate and stable HL map.
... proposed method can be applied to most segmentation methods with no and marginal extra cost during inference and training, respectively.
... method achieves consistent/significant improvement (1.37% mIoU on average) over most popular semantic segmentation methods on Cityscapes dataset, and demonstrates good generalization ability across domains ...
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