Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression. (arXiv:2204.00309v1 [cs.CV])

Learning from a label distribution has achieved promising results on ordinal
regression tasks such as facial age and head pose estimation wherein, the
concept of adaptive label distribution learning (ALDL) has drawn lots of
attention recently for its superiority in theory. However, compared with the
methods assuming fixed form label distribution, ALDL methods have not achieved
better performance. We argue that existing ALDL algorithms do not fully exploit
the intrinsic properties of ordinal regression. In this paper, we emphatically
summarize …

arxiv cv distribution learning loss ordinal regression