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CAR: Class-aware Regularizations for Semantic Segmentation
[article]
2022
arXiv
pre-print
Recent segmentation methods, such as OCR and CPNet, utilizing "class level" information in addition to pixel features, have achieved notable success for boosting the accuracy of existing network modules. However, the extracted class-level information was simply concatenated to pixel features, without explicitly being exploited for better pixel representation learning. Moreover, these approaches learn soft class centers based on coarse mask prediction, which is prone to error accumulation. In
arXiv:2203.07160v2
fatcat:jkzfce3tfbgjtgao5rzqqhm7na