FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction

Overview

FaceExtraction

FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction

Occlusions often occur in face images in the wild, troubling face-related tasks such as landmark detection, 3D reconstruction, and face recognition. It is beneficial to extract face regions from unconstrained face images accurately. However, current face segmentation datasets suffer from small data volumes, few occlusion types, low resolution, and imprecise annotation, limiting the performance of data-driven-based algorithms. This paper proposes a novel face occlusion dataset with manually labeled face occlusions from the CelebA-HQ and the internet. The occlusion types cover sunglasses, spectacles, hands, masks, scarfs, microphones, etc. To the best of our knowledge, it is by far the largest and most comprehensive face occlusion dataset. Combining it with the attribute mask in CelebAMask-HQ, we trained a straightforward face segmentation model but obtained SOTA performance, convincingly demonstrating the effectiveness of the proposed dataset.

Requirements

How to use

  1. Download CelebAMask-HQ dataset, detect the facial landmarks using 3DDFAv2
  2. Specify the directories in face_align/process_CelebAMaskHQ.py
  3. Run face_align/process_CelebAMaskHQ.py to generate&align CelebAMask-HQ images and masks 4.Download FaceOcc and put it under Dataset directory 5.Run train.py

Dataset

FaceOcc

Pretrained Model

Pretrained Model

Results

Face masks are shown in blue. From top to bottom are input images, predicted masks, and the ground truth: From top to the bottom: input images, predicted masks, ground truth

Citation

@misc{yin2022faceocc,
      title={FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction}, 
      author={Xiangnan Yin and Liming Chen},
      year={2022},
      eprint={2201.08425},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Learning Representational Invariances for Data-Efficient Action Recognition

Learning Representational Invariances for Data-Efficient Action Recognition Official PyTorch implementation for Learning Representational Invariances

Virginia Tech Vision and Learning Lab 27 Nov 22, 2022
The code for replicating the experiments from the LFI in SSMs with Unknown Dynamics paper.

Likelihood-Free Inference in State-Space Models with Unknown Dynamics This package contains the codes required to run the experiments in the paper. Th

Alex Aushev 0 Dec 27, 2021
Code for the IJCAI 2021 paper "Structure Guided Lane Detection"

SGNet Project for the IJCAI 2021 paper "Structure Guided Lane Detection" Abstract Recently, lane detection has made great progress with the rapid deve

Jinming Su 27 Dec 08, 2022
AFLNet: A Greybox Fuzzer for Network Protocols

AFLNet: A Greybox Fuzzer for Network Protocols AFLNet is a greybox fuzzer for protocol implementations. Unlike existing protocol fuzzers, it takes a m

626 Jan 06, 2023
A modern pure-Python library for reading PDF files

pdf A modern pure-Python library for reading PDF files. The goal is to have a modern interface to handle PDF files which is consistent with itself and

6 Apr 06, 2022
Differential fuzzing for the masses!

NEZHA NEZHA is an efficient and domain-independent differential fuzzer developed at Columbia University. NEZHA exploits the behavioral asymmetries bet

147 Dec 05, 2022
Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR

UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-

Microsoft 282 Jan 09, 2023
A short and easy PyTorch implementation of E(n) Equivariant Graph Neural Networks

Simple implementation of Equivariant GNN A short implementation of E(n) Equivariant Graph Neural Networks for HOMO energy prediction. Just 50 lines of

Arsenii Senya Ashukha 97 Dec 23, 2022
Vision Transformer for 3D medical image registration (Pytorch).

ViT-V-Net: Vision Transformer for Volumetric Medical Image Registration keywords: vision transformer, convolutional neural networks, image registratio

Junyu Chen 192 Dec 20, 2022
Machine Translation Implement By Bi-GRU And Transformer

Seq2Seq Translation Implement By Bidirectional GRU And Transformer In Pytorch Before You Run The Code You should download the data through the link be

He Wang 2 Oct 27, 2021
Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".

Dual Self-Attention Network for Multivariate Time Series Forecasting 20.10.26 Update: Due to the difficulty of installation and code maintenance cause

Kyon Huang 223 Dec 16, 2022
Title: Graduate-Admissions-Predictor

The purpose of this project is create a predictive model capable of identifying the probability of a person securing an admit based on their personal profile parameters. Simplified visualisations hav

Akarsh Singh 1 Jan 26, 2022
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning

TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning Authors: Yixuan Su, Fangyu Liu, Zaiqiao Meng, Lei Shu, Ehsan Shareghi, and Nig

Yixuan Su 79 Nov 04, 2022
Additional environments compatible with OpenAI gym

Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning A codebase for training reinforcement learning policies for quad

Zhehui Huang 40 Dec 06, 2022
Automatic tool focused on deriving metallicities of open clusters

metalcode Automatic tool focused on deriving metallicities of open clusters. Based on the method described in Pöhnl & Paunzen (2010, https://ui.adsabs

2 Dec 13, 2021
Learning to Estimate Hidden Motions with Global Motion Aggregation

Learning to Estimate Hidden Motions with Global Motion Aggregation (GMA) This repository contains the source code for our paper: Learning to Estimate

Shihao Jiang (Zac) 221 Dec 18, 2022
Code for "Adversarial Attack Generation Empowered by Min-Max Optimization", NeurIPS 2021

Min-Max Adversarial Attacks [Paper] [arXiv] [Video] [Slide] Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang, Tianyun Zha

Jingkang Wang 12 Nov 23, 2022
Code for "AutoMTL: A Programming Framework for Automated Multi-Task Learning"

AutoMTL: A Programming Framework for Automated Multi-Task Learning This is the website for our paper "AutoMTL: A Programming Framework for Automated M

Ivy Zhang 40 Dec 04, 2022
Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression.

Spatio-Temporal Entropy Model A Pytorch Reproduction of Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression. More details can

16 Nov 28, 2022
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)

MetaShift: A Dataset of Datasets for Evaluating Distribution Shifts and Training Conflicts This repo provides the PyTorch source code of our paper: Me

88 Jan 04, 2023