# Interlinked Convolutional Neural Networks for Face Parsing

arXiv: https://arxiv.org/abs/1806.02479 · HTML: https://mlsanity.com/paper/1806.02479/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 16 of 16)

- [HRFormer: High-Resolution Transformer for Dense Prediction](https://mlsanity.com/paper/2110.09408/) (2021)
- [Decoupled Multi-task Learning with Cyclical Self-Regulation for Face Parsing](https://mlsanity.com/paper/2203.14448/) (2022)
- [RoI Tanh-polar Transformer Network for Face Parsing in the Wild](https://mlsanity.com/paper/2102.02717/) (2021)
- [AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing](https://mlsanity.com/paper/2101.07034/) (2021)
- [Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN](https://mlsanity.com/paper/2212.09098/) (2022)
- [Parameter Efficient Local Implicit Image Function Network for Face Segmentation](https://mlsanity.com/paper/2303.15122/) (2023)
- [RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network](https://mlsanity.com/paper/2206.14098/) (2022)
- [Semantic Segmentation using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2209.08667/) (2022)
- [Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removal](https://mlsanity.com/paper/2104.09457/) (2021)
- [DisFaceRep: Representation Disentanglement for Co-occurring Facial Components in Weakly Supervised Face Parsing](https://mlsanity.com/paper/2508.01250/) (2025)
- [On the Effect of Image Resolution on Semantic Segmentation](https://mlsanity.com/paper/2402.05398/) (2024)
- [Shuffle Transformer with Feature Alignment for Video Face Parsing](https://mlsanity.com/paper/2106.08650/) (2021)
- [Occlusion-Aware Deep Convolutional Neural Network via Homogeneous Tanh-transforms for Face Parsing](https://mlsanity.com/paper/2308.15323/) (2023)
- [Multi-Scale Semantic Segmentation with Modified MBConv Blocks](https://mlsanity.com/paper/2402.04618/) (2024)
- [Early Fusion of Features for Semantic Segmentation](https://mlsanity.com/paper/2402.06091/) (2024)
- [S3POT: Contrast-Driven Face Occlusion Segmentation via Self-Supervised Prompt Learning](https://mlsanity.com/paper/2602.00635/) (2026)

## Abstract (arXiv)

Face parsing is a basic task in face image analysis. It amounts to labeling each pixel with appropriate facial parts such as eyes and nose. In the paper, we present a interlinked convolutional neural network (iCNN) for solving this problem in an end-to-end fashion. It consists of multiple convolutional neural networks (CNNs) taking input in different scales. A special interlinking layer is designed to allow the CNNs to exchange information, enabling them to integrate local and contextual information efficiently. The hallmark of iCNN is the extensive use of downsampling and upsampling in the interlinking layers, while traditional CNNs usually uses downsampling only. A two-stage pipeline is proposed for face parsing and both stages use iCNN. The first stage localizes facial parts in the size-reduced image and the second stage labels the pixels in the identified facial parts in the original image. On a benchmark dataset we have obtained better results than the state-of-the-art methods.