Face parsing is defined as the per-pixel labeling of images containing human
faces. The labels are defined to identify key facial regions like eyes, lips,
nose, hair, etc. In this work, we make use of the structural consistency of the
human face to propose a lightweight face-parsing method using a Local Implicit
Function network, FP-LIIF. We propose a simple architecture having a
convolutional encoder and a pixel MLP decoder that uses 1/26th number of
parameters compared to the state-of-the-art models and yet matches or
outperforms state-of-the-art models on multiple datasets, like CelebAMask-HQ
and LaPa. We do not use any pretraining, and compared to other works, our
network can also generate segmentation at different resolutions without any
changes in the input resolution. This work enables the use of facial
segmentation on low-compute or low-bandwidth devices because of its higher FPS
and smaller model size.

通过提出的本地隐式函数网络，我们在不预训练的情况下，提出了一种轻量级的人脸分割方法 FP-LIIF，使用卷积编码器和像素 MLP 解码器，并且相对于现有方法，我们的网络可以在不更改输入分辨率的情况下生成不同分辨率的分割，且模型尺寸小且速度更快，适用于低计算资源或低带宽设备。

适用于人脸分割的参数高效的本地隐式图像函数网络

Parameter Efficient Local Implicit Image Function Network for Face  Segmentation

With the advent of an increasing number of Augmented and Virtual Reality
applications that aim to perform meaningful and controlled style edits on
images of human faces, the impetus for the task of parsing face images to
produce accurate and fine-grained semantic segmentation maps is more than ever
before. Few State of the Art (SOTA) methods which solve this problem, do so by
incorporating priors with respect to facial structure or other face attributes
such as expression and pose in their deep classifier architecture. Our
endeavour in this work is to do away with the priors and complex pre-processing
operations required by SOTA multi-class face segmentation models by reframing
this operation as a downstream task post infusion of disentanglement with
respect to facial semantic regions of interest (ROIs) in the latent space of a
Generative Autoencoder model. We present results for our model's performance on
the CelebAMask-HQ and HELEN datasets. The encoded latent space of our model
achieves significantly higher disentanglement with respect to semantic ROIs
than that of other SOTA works. Moreover, it achieves a 13% faster inference
rate and comparable accuracy with respect to the publicly available SOTA for
the downstream task of semantic segmentation of face images.

本研究通过将 facial semantic ROIs 的语义划分和潜在的 Generative Autoencoder 模型相结合，实现了对面部图像语义细分的优化后任务，并在 CelebAMask-HQ 和 HELEN 数据集上展示了它比当前最先进的 works 具有更好的性能表现。

Latents2Segments：分离生成模型的潜在空间，用于面部图像的语义分割

Latents2Segments: Disentangling the Latent Space of Generative Models  for Semantic Segmentation of Face Images

Face parsing is an important computer vision task that requires accurate
pixel segmentation of facial parts (such as eyes, nose, mouth, etc.), providing
a basis for further face analysis, modification, and other applications.
Interlinked Convolutional Neural Networks (iCNN) was proved to be an effective
two-stage model for face parsing. However, the original iCNN was trained
separately in two stages, limiting its performance. To solve this problem, we
introduce a simple, end-to-end face parsing framework: STN-aided
iCNN(STN-iCNN), which extends the iCNN by adding a Spatial Transformer Network
(STN) between the two isolated stages. The STN-iCNN uses the STN to provide a
trainable connection to the original two-stage iCNN pipeline, making end-to-end
joint training possible. Moreover, as a by-product, STN also provides more
precise cropped parts than the original cropper. Due to these two advantages,
our approach significantly improves the accuracy of the original model. Our
model achieved competitive performance on the Helen Dataset, the standard face
parsing dataset. It also achieved superior performance on CelebAMask-HQ
dataset, proving its good generalization. Our code has been released at
this https URL

提出了一种基于 Spatial Transformer Network 的 end-to-end 面部分割框架，称为 STN-iCNN，通过在两个分离的训练阶段之间添加 STN，将原始 two-stage iCNN 连接起来，从而显着提高了面部分割的准确性。