mlsanityData to 7 Oct 2026

Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN

arXiv 2212.09098 ↗First posted 18 Dec 2022cs.CVcited by 16

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Abstract (arXiv)

Fine-grained semantic segmentation of a person's face and head, including facial parts and head components, has progressed a great deal in recent years. However, it remains a challenging task, whereby considering ambiguous occlusions and large pose variations are particularly difficult. To overcome these difficulties, we propose a novel framework termed Mask-FPAN. It uses a de-occlusion module that learns to parse occluded faces in a semi-supervised way. In particular, face landmark localization, face occlusionstimations, and detected head poses are taken into account. A 3D morphable face model combined with the UV GAN improves the robustness of 2D face parsing. In addition, we introduce two new datasets named FaceOccMask-HQ and CelebAMaskOcc-HQ for face paring work. The proposed Mask-FPAN framework addresses the face parsing problem in the wild and shows significant performance improvements with MIOU from 0.7353 to 0.9013 compared to the state-of-the-art on challenging face datasets.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 30 of its 48 references that are in the map. First posted on arXiv 18 Dec 2022.

Builds on

30 of 48 references.

References · 30

  1. 2017Mask R-CNNcited by 7,259
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  4. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  5. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  6. 2019MaskGAN: Towards Diverse and Interactive Facial Image Manipulationcited by 624
  7. 2018Image Inpainting for Irregular Holes Using Partial Convolutionscited by 505
  8. 2016ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentationcited by 291
  9. 2017ICNet for Real-Time Semantic Segmentation on High-Resolution Imagescited by 261
  10. 2017Fine-Grained Head Pose Estimation Without Keypointscited by 163
  11. 2018Look at Boundary: A Boundary-Aware Face Alignment Algorithmcited by 137
  12. 2019Adaptive Wing Loss for Robust Face Alignment via Heatmap Regressioncited by 127
18 more
  1. 2017Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networkscited by 108
  2. 2019TensorMask: A Foundation for Dense Object Segmentationcited by 101
  3. 2017UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognitioncited by 62
  4. 2018Style Aggregated Network for Facial Landmark Detectioncited by 60
  5. 2017Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascadecited by 53
  6. 2018Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectorscited by 49
  7. 2022Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learningcited by 47
  8. 2019Face Parsing with RoI Tanh-Warpingcited by 33
  9. 2022RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformercited by 31
  10. 2019PFLD: A Practical Facial Landmark Detectorcited by 25
  11. 2016Robust LSTM-Autoencoders for Face De-Occlusion in the Wildcited by 17
  12. 2018Interlinked Convolutional Neural Networks for Face Parsingcited by 16
  13. 2021AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsingcited by 15
  14. 2017Face Parsing via Recurrent Propagationcited by 14
  15. 2016Real-Time Facial Segmentation and Performance Capture from RGB Inputcited by 13
  16. 2016A CNN Cascade for Landmark Guided Semantic Part Segmentationcited by 12
  17. 2021Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomasscited by 11
  18. 2021LOTR: Face Landmark Localization Using Localization Transformercited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 9; 2025: 2; 2026: 1.

16 papers.

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  11. 2023Edge Aware Learning for 3D Point Cloudcited by 3
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  4. 2024MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencodercited by 0