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kartiknarayan/
SegFace

2412.08647

mit

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SegFace Model Card

Project Page | Paper (ArXiv) | Code

Introduction

The key contributions of our work are,

We introduce a lightweight transformer decoder with learnable class-specific tokens, that ensures each token is dedicated to a specific class, thereby enabling independent modeling of classes. The design effectively addresses the challenge of poor segmentation performance of long-tail classes, prevalent in existing methods.
Our multi-scale feature extraction and MLP fusion strategy, combined with a transformer decoder that leverages learnable class-specific tokens, mitigates the dominance of head classes during training and enhances the feature representation of long-tail classes.
SegFace establishes a new state-of-the-art performance on the LaPa dataset (93.03 mean F1 score) and the CelebAMask-HQ dataset (88.96 mean F1 score). Moreover, our model can be adapted for fast inference by simply swapping the backbone with a MobileNetV3 backbone. The mobile version achieves a mean F1 score of 87.91 on the CelebAMask-HQ dataset with 95.96 FPS.

Training Framework

The proposed architecture, SegFace, addresses face segmentation by enhancing the performance on long-tail classes through a transformer-based approach. Specifically, multi-scale features are first extracted from an image encoder and then fused using an MLP fusion module to form face tokens. These tokens, along with class-specific tokens, undergo self-attention, face-to-token, and token-to-face cross-attention operations, refining both class and face tokens to enhance class-specific features. Finally, the upscaled face tokens and learned class tokens are combined to produce segmentation maps for each facial region.

Usage

The trained weights can be downloaded directly from this repository or using python:

python

from huggingface_hub import hf_hub_download

# The filename "convnext_celeba_512" indicates that the model has a convnext bakcbone and trained
# on celeba dataset at 512 resolution.
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="convnext_celeba_512/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="efficientnet_celeba_512/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="mobilenet_celeba_512/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="resnet_celeba_512/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinb_celeba_224/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinb_celeba_256/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinb_celeba_448/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinb_celeba_512/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinb_lapa_224/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinb_lapa_256/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinb_lapa_448/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinb_lapa_512/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinv2b_celeba_512/model_299.pt", local_dir="./weights")
hf_hub_download(repo_id="kartiknarayan/SegFace", filename="swinb_helen_512/model_299.pt", local_dir="./weights")

Citation

bibtex

@inproceedings{narayan2025segface,
  title={Segface: Face segmentation of long-tail classes},
  author={Narayan, Kartik and Vs, Vibashan and Patel, Vishal M},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={6},
  pages={6182--6190},
  year={2025}
}

Please check our GitHub repository for complete instructions.