mlsanityData to 14 Oct 2026

Parameter Efficient Local Implicit Image Function Network for Face Segmentation

arXiv 2303.15122 ↗First posted 27 Mar 2023cs.CVcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 46 of its 63 references that are in the map. First posted on arXiv 27 Mar 2023.

Builds on

46 of 63 references.

References · 46

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34 more
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Built on by

Cited by year (2026 to 14 Oct)

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

8 papers.

  1. 2024FaceXFormer: A Unified Transformer for Facial Analysiscited by 34
  2. 2025FaceXBench: Evaluating Multimodal LLMs on Face Understandingcited by 24
  3. 2025StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representationcited by 3
  4. 2025Pixels2Points: Fusing 2D and 3D Features for Facial Skin Segmentationcited by 2
  5. 2025DisFaceRep: Representation Disentanglement for Co-occurring Facial Components in Weakly Supervised Face Parsingcited by 2
  6. 2024SynthForge: Synthesizing High-Quality Face Dataset with Controllable 3D Generative Modelscited by 1
  7. 2026How Far Can INRs Go? Cross-Domain Parameter-efficient INR-Based Semantic Segmentation for Brain MRIcited by 0
  8. 2026DIME: Scaling Facial Representation Learning via Differential Masked Autoencodingcited by 0