# A CNN Cascade for Landmark Guided Semantic Part Segmentation

arXiv: https://arxiv.org/abs/1609.09642 · HTML: https://mlsanity.com/paper/1609.09642/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2016.

## Built on by (most cited first, 12 of 12)

- [Unsupervised Part Discovery from Contrastive Reconstruction](https://mlsanity.com/paper/2111.06349/) (2021)
- [FaceXFormer: A Unified Transformer for Facial Analysis](https://mlsanity.com/paper/2403.12960/) (2024)
- [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)
- [FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild](https://mlsanity.com/paper/2106.11145/) (2021)
- [Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN](https://mlsanity.com/paper/2212.09098/) (2022)
- [Face-MLLM: A Large Face Perception Model](https://mlsanity.com/paper/2410.20717/) (2024)
- [Parameter Efficient Local Implicit Image Function Network for Face Segmentation](https://mlsanity.com/paper/2303.15122/) (2023)
- [DisFaceRep: Representation Disentanglement for Co-occurring Facial Components in Weakly Supervised Face Parsing](https://mlsanity.com/paper/2508.01250/) (2025)
- [3D Face Parsing via Surface Parameterization and 2D Semantic Segmentation Network](https://mlsanity.com/paper/2206.09221/) (2022)
- [DenseSeg: Joint Learning for Semantic Segmentation and Landmark Detection Using Dense Image-to-Shape Representation](https://mlsanity.com/paper/2405.19746/) (2024)
- [A Facial Feature Discovery Framework for Race Classification Using Deep Learning](https://mlsanity.com/paper/2104.02471/) (2021)

## Abstract (arXiv)

This paper proposes a CNN cascade for semantic part segmentation guided by pose-specific information encoded in terms of a set of landmarks (or keypoints). There is large amount of prior work on each of these tasks separately, yet, to the best of our knowledge, this is the first time in literature that the interplay between pose estimation and semantic part segmentation is investigated. To address this limitation of prior work, in this paper, we propose a CNN cascade of tasks that firstly performs landmark localisation and then uses this information as input for guiding semantic part segmentation. We applied our architecture to the problem of facial part segmentation and report large performance improvement over the standard unguided network on the most challenging face datasets. Testing code and models will be published online at http://cs.nott.ac.uk/~psxasj/.