Neuron Structure Modeling for Generalizable Remote Physiological Measurement

• URL: http://arxiv.org/abs/2303.05955v1
• Date: Fri, 10 Mar 2023 14:44:11 GMT
• Title: Neuron Structure Modeling for Generalizable Remote Physiological Measurement
• Authors: Hao Lu, Zitong Yu, Xuesong Niu, Yingcong Chen
• Abstract summary: Remote photoplethysmography (r) technology has drawn increasing attention in recent years. It can extract Blood Volume Pulse (BVP) from facial videos, making many applications more accessible. Existing methods struggle to generalize well for unseen domains. We propose a domain-label-free approach called NEuron STructure modeling (NEST)
• Score: 35.33213338840912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote photoplethysmography (rPPG) technology has drawn increasing attention in recent years. It can extract Blood Volume Pulse (BVP) from facial videos, making many applications like health monitoring and emotional analysis more accessible. However, as the BVP signal is easily affected by environmental changes, existing methods struggle to generalize well for unseen domains. In this paper, we systematically address the domain shift problem in the rPPG measurement task. We show that most domain generalization methods do not work well in this problem, as domain labels are ambiguous in complicated environmental changes. In light of this, we propose a domain-label-free approach called NEuron STructure modeling (NEST). NEST improves the generalization capacity by maximizing the coverage of feature space during training, which reduces the chance for under-optimized feature activation during inference. Besides, NEST can also enrich and enhance domain invariant features across multi-domain. We create and benchmark a large-scale domain generalization protocol for the rPPG measurement task. Extensive experiments show that our approach outperforms the state-of-the-art methods on both cross-dataset and intra-dataset settings.

Related papers

• Gradient Alignment for Cross-Domain Face Anti-Spoofing [26.517887637150594]
  We introduce GAC-FAS, a novel learning objective that encourages the model to converge towards an optimal flat minimum. Unlike conventional sharpness-aware minimizers, GAC-FAS identifies ascending points for each domain and regulates the generalization gradient updates. We demonstrate the efficacy of GAC-FAS through rigorous testing on challenging cross-domain FAS datasets.
  arXiv  Detail & Related papers  (2024-02-29T02:57:44Z)
• Single Domain Dynamic Generalization for Iris Presentation Attack Detection [41.126916126040655]
  Iris presentation generalization has achieved great success under intra-domain settings but easily degrades on unseen domains. We propose a Single Domain Dynamic Generalization (SDDG) framework, which exploits domain-invariant and domain-specific features on a per-sample basis. The proposed method is effective and outperforms the state-of-the-art on LivDet-Iris 2017 dataset.
  arXiv  Detail & Related papers  (2023-05-22T07:54:13Z)
• Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains [3.9498537297431167]
  generative feature-driven image replay in conjunction with a dual-purpose discriminator enables the generation of images with realistic features for replay. We present detailed ablation experiments studying our proposed method components and demonstrate a possible use-case of our continual UDA method for an unsupervised patch-based segmentation task.
  arXiv  Detail & Related papers  (2023-02-02T18:19:01Z)
• Cyclically Disentangled Feature Translation for Face Anti-spoofing [61.70377630461084]
  We propose a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN) CDFTN generates pseudo-labeled samples that possess: 1) source domain-invariant liveness features and 2) target domain-specific content features, which are disentangled through domain adversarial training. A robust classifier is trained based on the synthetic pseudo-labeled images under the supervision of source domain labels.
  arXiv  Detail & Related papers  (2022-12-07T14:12:34Z)
• Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts [133.99270341855728]
  Real-world domain styles can vary substantially due to environment changes and sensor noises. Deep models only know the training domain style. We propose Normalization Perturbation to overcome this domain style overfitting problem.
  arXiv  Detail & Related papers  (2022-11-08T17:36:49Z)
• AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation [1.0452185327816181]
  We propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG) Our AADG framework can effectively sample data augmentation policies that generate novel domains. Our proposed AADG exhibits state-of-the-art generalization performance and outperforms existing approaches.
  arXiv  Detail & Related papers  (2022-07-27T02:26:01Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing [53.82826073959756]
  Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios. We propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels. To overcome the limitation, we propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels.
  arXiv  Detail & Related papers  (2021-05-06T06:04:59Z)
• Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentation [1.1799563040751586]
  We present a novel unsupervised domain adaptation network for generalizing models learned from the labeled source domain to the unlabeled target domain. Specifically, our approach consists of two key modules, a conditional domain discriminator(CDD) and a category-centric prototype aligner(CCPA)
  arXiv  Detail & Related papers  (2021-03-03T07:07:38Z)
• Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains [68.73614619875814]
  We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains.
  arXiv  Detail & Related papers  (2020-07-04T07:56:02Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)

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.