STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection
Zhenglin Zhou Huaxia Li Hong Liu Nanyang Wang Gang Yu Rongrong Ji
Recently, deep learning-based facial landmark detection has achieved
significant improvement. However, the semantic ambiguity problem degrades
detection performance. Specifically, the semantic ambiguity causes inconsistent
annotation and negatively affects the model's convergence, leading to worse
accuracy and instability prediction. To solve this problem, we propose a
Self-adapTive Ambiguity Reduction (STAR) loss by exploiting the properties of
semantic ambiguity. We find that semantic ambiguity results in the anisotropic
predicted distribution, which inspires us to use predicted distribution to
represent semantic ambiguity. Based on this, we design the STAR loss that
measures the anisotropism of the predicted distribution. Compared with the
standard regression loss, STAR loss is encouraged to be small when the
predicted distribution is anisotropic and thus adaptively mitigates the impact
of semantic ambiguity. Moreover, we propose two kinds of eigenvalue restriction
methods that could avoid both distribution's abnormal change and the model's
premature convergence. Finally, the comprehensive experiments demonstrate that
STAR loss outperforms the state-of-the-art methods on three benchmarks, i.e.,
COFW, 300W, and WFLW, with negligible computation overhead. Code is at
https://github.com/ZhenglinZhou/STAR.