Chrome Extension
WeChat Mini Program
Use on ChatGLM

To See Facial Expressions Through Occlusions via Adversarial Disentangled Features Learning with 3D Supervision

Chinese Conference on Biometric Recognition (SINOBIOMETRICS)(2021)

Sichuan Univ

Cited 1|Views10
Abstract
Facial expression recognition (FER) is still a challenging problem if face images are contaminated by occlusions, which lead to not only noisy features but also loss of discriminative features. To address the issue, this paper proposes a novel adversarial disentangled features learning (ADFL) method for recognizing expressions on occluded face images. Unlike previous methods, our method defines an explicit noise component in addition to the identity and expression components to isolate the occlusion-caused noise features. Besides, we learn shape features with joint supervision of 3D shape reconstruction and facial expression recognition to compensate for the occlusion-caused loss of features. Evaluation on both in-the-lab and in-the-wild face images demonstrates that our proposed method effectively improves FER accuracy for occluded images, and can even deal with noise beyond occlusions.
More
Translated text
Key words
Facial expression recognition, Occlusions, Feature disentanglement, Adversarial learning, Shape features
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest