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Margin Calibration for Long-Tailed Visual Recognition
[article]
2022
arXiv
pre-print
The long-tailed class distribution in visual recognition tasks poses great challenges for neural networks on how to handle the biased predictions between head and tail classes, i.e., the model tends to classify tail classes as head classes. While existing research focused on data resampling and loss function engineering, in this paper, we take a different perspective: the classification margins. We study the relationship between the margins and logits (classification scores) and empirically
arXiv:2112.07225v5
fatcat:eyk5cvrywrez5l436xiytkwsoa