Bi-TTA: Bidirectional Test-Time Adapter for Remote Physiological Measurement

• URL: http://arxiv.org/abs/2409.17316v1
• Date: Wed, 25 Sep 2024 19:55:20 GMT
• Title: Bi-TTA: Bidirectional Test-Time Adapter for Remote Physiological Measurement
• Authors: Haodong Li, Hao Lu, Ying-Cong Chen,
• Abstract summary: Remote photoplethysmography (r) is gaining prominence for its non-invasive approach to monitoring physiological signals using only cameras. Despite its promise, the adaptability of r models to new domains is hindered due to the environmental sensitivity of physiological signals. We present Bi-TTA, a novel expert knowledge-based Bidirectional Test-Time Adapter framework.
• Score: 25.11883761217408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote photoplethysmography (rPPG) is gaining prominence for its non-invasive approach to monitoring physiological signals using only cameras. Despite its promise, the adaptability of rPPG models to new, unseen domains is hindered due to the environmental sensitivity of physiological signals. To address this, we pioneer the Test-Time Adaptation (TTA) in rPPG, enabling the adaptation of pre-trained models to the target domain during inference, sidestepping the need for annotations or source data due to privacy considerations. Particularly, utilizing only the user's face video stream as the accessible target domain data, the rPPG model is adjusted by tuning on each single instance it encounters. However, 1) TTA algorithms are designed predominantly for classification tasks, ill-suited in regression tasks such as rPPG due to inadequate supervision. 2) Tuning pre-trained models in a single-instance manner introduces variability and instability, posing challenges to effectively filtering domain-relevant from domain-irrelevant features while simultaneously preserving the learned information. To overcome these challenges, we present Bi-TTA, a novel expert knowledge-based Bidirectional Test-Time Adapter framework. Specifically, leveraging two expert-knowledge priors for providing self-supervision, our Bi-TTA primarily comprises two modules: a prospective adaptation (PA) module using sharpness-aware minimization to eliminate domain-irrelevant noise, enhancing the stability and efficacy during the adaptation process, and a retrospective stabilization (RS) module to dynamically reinforce crucial learned model parameters, averting performance degradation caused by overfitting or catastrophic forgetting. To this end, we established a large-scale benchmark for rPPG tasks under TTA protocol. The experimental results demonstrate the significant superiority of our approach over the state-of-the-art.

Related papers

• DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation [10.127634263641877]
  Adapting machine learning models to new domains without labeled data is a critical challenge in applications like medical imaging, autonomous driving, and remote sensing. This task, known as Source-Free Unsupervised Domain Adaptation (SFUDA), involves adapting a pre-trained model to a target domain using only unlabeled target data. Existing SFUDA methods often rely on single-model architectures, struggling with uncertainty and variability in the target domain. We propose DRIVE, a novel SFUDA framework leveraging a dual-model architecture. The two models, with identical weights, work in parallel to capture diverse target domain characteristics.
  arXiv  Detail & Related papers  (2024-11-24T20:35:04Z)
• Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation Challenge [0.9854844969061186]
  The third autoPET challenge introduced a new data-centric task this year. This task shifted the focus from model development to improving metastatic lesion segmentation on PET/CT images. We developed methods to enhance segmentation performance tailored to the characteristics of PET/CT imaging.
  arXiv  Detail & Related papers  (2024-09-16T09:32:04Z)
• Enhancing Test Time Adaptation with Few-shot Guidance [35.13317598777832]
  Deep neural networks often encounter significant performance drops while facing with domain shifts between training (source) and test (target) data. Test Time Adaptation (TTA) methods have been proposed to adapt pre-trained source model to handle out-of-distribution streaming target data. We develop Few-Shot Test Time Adaptation (FS-TTA), a novel and practical setting that utilizes a few-shot support set on top of TTA.
  arXiv  Detail & Related papers  (2024-09-02T15:50:48Z)
• Fully Test-Time rPPG Estimation via Synthetic Signal-Guided Feature Learning [8.901227918730562]
  TestTime Adaptation (TTA) enables the model to adaptively estimate r signals in various unseen domains by online adapting to unlabeled target data without referring to any source data. We develop a synthetic signal-guided feature learning method by pseudo r signals as pseudo ground truths to guide a conditional generator in generating latent r features.
  arXiv  Detail & Related papers  (2024-07-18T09:22:40Z)
• Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation [49.53202761595912]
  Continual Test-Time Adaptation involves adapting a pre-trained source model to continually changing unsupervised target domains. We analyze the challenges of this task: online environment, unsupervised nature, and the risks of error accumulation and catastrophic forgetting. We propose an uncertainty-aware buffering approach to identify and aggregate significant samples with high certainty from the unsupervised, single-pass data stream.
  arXiv  Detail & Related papers  (2024-07-12T15:48:40Z)
• Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments [13.163784646113214]
  Continual Test-Time Adaptation (CTTA) has recently emerged as a promising technique to gradually adapt a source-trained model to continually changing target domains. We present AMROD, featuring three core components. Firstly, the object-level contrastive learning module extracts object-level features for contrastive learning to refine the feature representation in the target domain. Secondly, the adaptive monitoring module dynamically skips unnecessary adaptation and updates the category-specific threshold based on predicted confidence scores to enable efficiency and improve the quality of pseudo-labels.
  arXiv  Detail & Related papers  (2024-06-24T08:30:03Z)
• Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer [54.32283739486781]
  We present a textbfForgery-aware textbfAdaptive textbfVision textbfTransformer (FA-ViT) under the adaptive learning paradigm. FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation.
  arXiv  Detail & Related papers  (2023-09-20T06:51:11Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation [48.039156140237615]
  A Continual Test-Time Adaptation task is proposed to adapt the pre-trained model to continually changing target domains. We design a Visual Domain Adapter (ViDA) for CTTA, explicitly handling both domain-specific and domain-shared knowledge. Our proposed method achieves state-of-the-art performance in both classification and segmentation CTTA tasks.
  arXiv  Detail & Related papers  (2023-06-07T11:18:53Z)
• Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation [62.458968086881555]
  Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains. We propose a Confidence-Attentive network with geneRalization enhanced self-knowledge disTillation (CART) to address the challenge in CVDA.
  arXiv  Detail & Related papers  (2023-03-18T16:40:10Z)
• Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction [94.25865526414717]
  This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos. We propose Bilevel Online Adaptation, which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks.
  arXiv  Detail & Related papers  (2021-03-30T15:47:58Z)

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.