Adaptive Graph-Based Feature Normalization for Facial Expression Recognition

• URL: http://arxiv.org/abs/2207.11123v1
• Date: Fri, 22 Jul 2022 14:57:56 GMT
• Title: Adaptive Graph-Based Feature Normalization for Facial Expression Recognition
• Authors: Yangtao Du and Qingqing Wang and Yujie Xiong
• Abstract summary: We propose an Adaptive Graph-based Feature Normalization (AGFN) method to protect Facial Expression Recognition models from data uncertainties. Our method outperforms state-of-the-art works with accuracies of 91.84% and 91.11% on benchmark datasets.
• Score: 1.2246649738388389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial Expression Recognition (FER) suffers from data uncertainties caused by ambiguous facial images and annotators' subjectiveness, resulting in excursive semantic and feature covariate shifting problem. Existing works usually correct mislabeled data by estimating noise distribution, or guide network training with knowledge learned from clean data, neglecting the associative relations of expressions. In this work, we propose an Adaptive Graph-based Feature Normalization (AGFN) method to protect FER models from data uncertainties by normalizing feature distributions with the association of expressions. Specifically, we propose a Poisson graph generator to adaptively construct topological graphs for samples in each mini-batches via a sampling process, and correspondingly design a coordinate descent strategy to optimize proposed network. Our method outperforms state-of-the-art works with accuracies of 91.84% and 91.11% on the benchmark datasets FERPlus and RAF-DB, respectively, and when the percentage of mislabeled data increases (e.g., to 20%), our network surpasses existing works significantly by 3.38% and 4.52%.

Related papers

• Endowing Pre-trained Graph Models with Provable Fairness [49.8431177748876]
  We propose a novel adapter-tuning framework that endows pre-trained graph models with provable fairness (called GraphPAR) Specifically, we design a sensitive semantic augmenter on node representations, to extend the node representations with different sensitive attribute semantics for each node. With GraphPAR, we quantify whether the fairness of each node is provable, i.e., predictions are always fair within a certain range of sensitive attribute semantics.
  arXiv  Detail & Related papers  (2024-02-19T14:16:08Z)
• Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiency [19.49531172542614]
  We propose a unified framework to exploit the Probability of Necessity and Sufficiency to extract the Invariant Substructure (PNSIS) Our model outperforms the state-of-the-art techniques on graph OOD on several benchmarks.
  arXiv  Detail & Related papers  (2024-02-14T13:31:53Z)
• Chasing Fairness in Graphs: A GNN Architecture Perspective [73.43111851492593]
  We propose textsfFair textsfMessage textsfPassing (FMP) designed within a unified optimization framework for graph neural networks (GNNs) In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets.
  arXiv  Detail & Related papers  (2023-12-19T18:00:15Z)
• Graph Out-of-Distribution Generalization with Controllable Data Augmentation [51.17476258673232]
  Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. Due to the selection bias of training and testing data, distribution deviation is widespread. We propose OOD calibration to measure the distribution deviation of virtual samples.
  arXiv  Detail & Related papers  (2023-08-16T13:10:27Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Features Based Adaptive Augmentation for Graph Contrastive Learning [0.0]
  Self-Supervised learning aims to eliminate the need for expensive annotation in graph representation learning. We introduce a Feature Based Adaptive Augmentation (FebAA) approach, which identifies and preserves potentially influential features. We successfully improved the accuracy of GRACE and BGRL on eight graph representation learning's benchmark datasets.
  arXiv  Detail & Related papers  (2022-07-05T03:41:20Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition [46.99756911719854]
  We achieve uncertain label correction of facial expressions using auxiliary action unit (AU) graphs, called ULC-AG. Experiments show that our ULC-AG achieves 89.31% and 61.57% accuracy on RAF-DB and AffectNet datasets, respectively.
  arXiv  Detail & Related papers  (2022-04-23T11:09:43Z)
• Effective Eigendecomposition based Graph Adaptation for Heterophilic Networks [0.5309004257911242]
  We present an eigendecomposition based approach and propose EigenNetwork models that improve the performance of GNNs on heterophilic graphs. Our approach achieves up to 11% improvement in performance over the state-of-the-art methods on heterophilic graphs.
  arXiv  Detail & Related papers  (2021-07-28T12:14:07Z)
• Robust Optimization as Data Augmentation for Large-scale Graphs [117.2376815614148]
  We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training. FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks.
  arXiv  Detail & Related papers  (2020-10-19T21:51:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.