3 accepted papers
We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. While contrastive learning effectively leverages all samples in a batch, it typically ignores the inherent ordering among …
Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption. In this paper, we reformulate rank estimation with noisy ordinal labels as a stochastic ordering problem, in which each instance is inhere…
In recent interactive segmentation algorithms previous probability maps are used as network input to help predictions in the current segmentation round. However despite the utilization of previous masks useful information contained in the probability maps is not well propagated to the current predic…