  We propose Class Based Thresholding (CBT) to reduce the computational cost of
early exit semantic segmentation models while preserving the mean intersection
over union (mIoU) performance. A key idea of CBT is to exploit the
naturally-occurring neural collapse phenomenon. Specifically, by calculating
the mean prediction probabilities of each class in the training set, CBT
assigns different masking threshold values to each class, so that the
computation can be terminated sooner for pixels belonging to easy-to-predict
classes. We show the effectiveness of CBT on Cityscapes and ADE20K datasets.
CBT can reduce the computational cost by $23\%$ compared to the previous
state-of-the-art early exit models.
