ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning

• URL: http://arxiv.org/abs/2305.01486v5
• Date: Thu, 24 Oct 2024 09:32:17 GMT
• Title: ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning
• Abstract summary: ARBEx is a novel attentive feature extraction framework driven by Vision Transformer. We employ learnable anchor points in the embedding space with label distributions and multi-head self-attention mechanism to optimize performance against weak predictions. Our strategy outperforms current state-of-the-art methodologies, according to extensive experiments conducted in a variety of contexts.
• Score: 5.648318448953635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a framework ARBEx, a novel attentive feature extraction framework driven by Vision Transformer with reliability balancing to cope against poor class distributions, bias, and uncertainty in the facial expression learning (FEL) task. We reinforce several data pre-processing and refinement methods along with a window-based cross-attention ViT to squeeze the best of the data. We also employ learnable anchor points in the embedding space with label distributions and multi-head self-attention mechanism to optimize performance against weak predictions with reliability balancing, which is a strategy that leverages anchor points, attention scores, and confidence values to enhance the resilience of label predictions. To ensure correct label classification and improve the models' discriminative power, we introduce anchor loss, which encourages large margins between anchor points. Additionally, the multi-head self-attention mechanism, which is also trainable, plays an integral role in identifying accurate labels. This approach provides critical elements for improving the reliability of predictions and has a substantial positive effect on final prediction capabilities. Our adaptive model can be integrated with any deep neural network to forestall challenges in various recognition tasks. Our strategy outperforms current state-of-the-art methodologies, according to extensive experiments conducted in a variety of contexts.

Related papers

• Robustifying Vision-Language Models via Test-Time Prompt Adaptation [23.05168102474528]
  We propose RITA, a test-tIme prompt-TAdaptation framework that shifts from sample-level estimates to distribution-level alignment.<n>Specifically, RITA employs optimal transport to align the distribution of augmented visual features with textual prototypes, mitigating adversarial outliers and rectifying cross-modal semantic misalignment.
  arXiv  Detail & Related papers  (2026-07-10T14:19:42Z)
• From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models [77.04403907729738]
  This survey charts the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.<n>We demonstrate how uncertainty is leveraged as an active control signal across three frontiers.<n>This survey argues that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
  arXiv  Detail & Related papers  (2026-01-22T06:21:31Z)
• SG-OIF: A Stability-Guided Online Influence Framework for Reliable Vision Data [6.4391040754741296]
  In this paper, we introduce a Stability-Guided Online Influence Framework (SG-OIF) for Approximating training-point influence on test predictions.<n>We show that SG-OIF achieves 91.1% accuracy in the top 1% prediction samples on the CIFAR-10, and 99.8% AUPR score on MNIST.
  arXiv  Detail & Related papers  (2025-11-21T19:58:54Z)
• Reliable and Reproducible Demographic Inference for Fairness in Face Analysis [63.46525489354455]
  We propose a fully reproducible DAI pipeline that replaces conventional end-to-end training with a modular transfer learning approach.<n>We audit this pipeline across three dimensions: accuracy, fairness, and a newly introduced notion of robustness, defined via intra-identity consistency.<n>Our results show that the proposed method outperforms strong baselines, particularly on ethnicity, which is the more challenging attribute.
  arXiv  Detail & Related papers  (2025-10-23T12:22:02Z)
• Uncertainty Awareness on Unsupervised Domain Adaptation for Time Series Data [49.36938105983916]
  Unsupervised domain adaptation methods seek to generalize effectively on unlabeled test data.<n>We propose incorporating multi-scale feature extraction and uncertainty estimation to improve the model's generalization and robustness across domains.
  arXiv  Detail & Related papers  (2025-08-26T03:13:08Z)
• Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation [5.7977777220041204]
  A common practice in pseudo-supervision is filtering pseudo-labels based on pre-defined confidence thresholds or entropy.<n>We propose Ensemble-of-Confidence Reinforcement (ENCORE), a dynamic feedback-driven thresholding strategy for pseudo-label selection.<n>Our method seamlessly integrates into existing pseudo-supervision frameworks and significantly improves segmentation performance.
  arXiv  Detail & Related papers  (2025-05-12T15:58:08Z)
• Towards Efficient and General-Purpose Few-Shot Misclassification Detection for Vision-Language Models [25.51735861729728]
  Modern neural networks often exhibit overconfidence for misclassified predictions, highlighting the need for confidence estimation to detect errors. We exploit vision language model (VLM) leveraging text information to establish an efficient and general-purpose misclassification detection framework. By harnessing the power of VLM, we construct FSMisD, a Few-Shot prompt learning framework for MisD to refrain from training from scratch and therefore improve tuning efficiency.
  arXiv  Detail & Related papers  (2025-03-26T12:31:04Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy. We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods. By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation [2.28438857884398]
  This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a consensus-based verification process integrated with an adaptive thresholding mechanism. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
  arXiv  Detail & Related papers  (2024-03-05T20:54:56Z)
• The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
  Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2024-01-25T09:18:51Z)
• Boosting Adversarial Robustness using Feature Level Stochastic Smoothing [46.86097477465267]
  adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. In this work, we propose a generic method for introducingity in the network predictions. We also utilize this for smoothing decision rejecting low confidence predictions.
  arXiv  Detail & Related papers  (2023-06-10T15:11:24Z)
• Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection [2.5193191501662144]
  Unsupervised Domain Adaptive Object Detection (UDA-OD) uses unlabelled data to improve the reliability of robotic vision systems in open-world environments. Previous approaches to UDA-OD based on self-training have been effective in overcoming changes in the general appearance of images. We propose a framework for explicitly addressing class distribution shift to improve pseudo-label reliability in self-training.
  arXiv  Detail & Related papers  (2023-02-13T00:46:34Z)
• Fairness-aware Vision Transformer via Debiased Self-Attention [12.406960223371959]
  Debiased Self-Attention (DSA) is a fairness-through-blindness approach that enforces Vision Transformer (ViT) to eliminate spurious features correlated with the sensitive label for bias mitigation. Our framework leads to improved fairness guarantees over prior works on multiple prediction tasks without compromising target prediction performance.
  arXiv  Detail & Related papers  (2023-01-31T17:44:59Z)
• Trustworthy Representation Learning via Information Funnels and Bottlenecks [6.750492479021436]
  We introduce Conditional Privacy Funnel with Side-information (CPFSI)<n>CPFSI is applicable in both fully and semi-supervised settings.<n>We show that CPFSI effectively balances competing objectives and frequently outperforms existing approaches.
  arXiv  Detail & Related papers  (2022-11-02T19:37:55Z)
• Trusted Multi-View Classification with Dynamic Evidential Fusion [73.35990456162745]
  We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC) TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
  arXiv  Detail & Related papers  (2022-04-25T03:48:49Z)
• Trusted Multi-View Classification [76.73585034192894]
  We propose a novel multi-view classification method, termed trusted multi-view classification. It provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed algorithm jointly utilizes multiple views to promote both classification reliability and robustness.
  arXiv  Detail & Related papers  (2021-02-03T13:30:26Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)
• Robust Pre-Training by Adversarial Contrastive Learning [120.33706897927391]
  Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness. We improve robustness-aware self-supervised pre-training by learning representations consistent under both data augmentations and adversarial perturbations.
  arXiv  Detail & Related papers  (2020-10-26T04:44:43Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)

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.