  This paper probes intrinsic factors behind typical failure cases (e.g.
spatial inconsistency and boundary confusion) produced by the existing
state-of-the-art method in face parsing. To tackle these problems, we propose a
novel Decoupled Multi-task Learning with Cyclical Self-Regulation (DML-CSR) for
face parsing. Specifically, DML-CSR designs a multi-task model which comprises
face parsing, binary edge, and category edge detection. These tasks only share
low-level encoder weights without high-level interactions between each other,
enabling to decouple auxiliary modules from the whole network at the inference
stage. To address spatial inconsistency, we develop a dynamic dual graph
convolutional network to capture global contextual information without using
any extra pooling operation. To handle boundary confusion in both single and
multiple face scenarios, we exploit binary and category edge detection to
jointly obtain generic geometric structure and fine-grained semantic clues of
human faces. Besides, to prevent noisy labels from degrading model
generalization during training, cyclical self-regulation is proposed to
self-ensemble several model instances to get a new model and the resulting
model then is used to self-distill subsequent models, through alternating
iterations. Experiments show that our method achieves the new state-of-the-art
performance on the Helen, CelebAMask-HQ, and Lapa datasets. The source code is
available at
https://github.com/deepinsight/insightface/tree/master/parsing/dml_csr.
