Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection

• URL: http://arxiv.org/abs/2509.12990v2
• Date: Fri, 03 Oct 2025 15:18:16 GMT
• Title: Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection
• Authors: Boyu Han, Qianqian Xu, Shilong Bao, Zhiyong Yang, Sicong Li, Qingming Huang,
• Abstract summary: We propose a Dual-Stage Reweighted Mixture-of-Experts (DR-MoE) framework to handle the challenges posed by subtle and infrequent mistakes.<n>The proposed method achieves strong performance, particularly in identifying rare and ambiguous mistake instances.
• Score: 85.0189917888094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this report, we address the problem of determining whether a user performs an action incorrectly from egocentric video data. To handle the challenges posed by subtle and infrequent mistakes, we propose a Dual-Stage Reweighted Mixture-of-Experts (DR-MoE) framework. In the first stage, features are extracted using a frozen ViViT model and a LoRA-tuned ViViT model, which are combined through a feature-level expert module. In the second stage, three classifiers are trained with different objectives: reweighted cross-entropy to mitigate class imbalance, AUC loss to improve ranking under skewed distributions, and label-aware loss with sharpness-aware minimization to enhance calibration and generalization. Their predictions are fused using a classification-level expert module. The proposed method achieves strong performance, particularly in identifying rare and ambiguous mistake instances. The code is available at https://github.com/boyuh/DR-MoE.

Related papers

• Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification [59.59359638389348]
  We propose a Dual-level Modality Debiasing Learning framework that implements debiasing at both the model and optimization levels.<n>Experiments on benchmark datasets demonstrate that DMDL could enable modality-invariant feature learning and a more generalized model.
  arXiv  Detail & Related papers  (2025-12-03T12:43:16Z)
• DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification [11.539700200482853]
  ETSC is critical in time-sensitive medical applications such as sepsis.<n>It presents an inherent trade-off between accuracy and earliness.<n>We propose textbfDE3S, a framework to overcome these underlying challenges.
  arXiv  Detail & Related papers  (2025-10-14T07:10:05Z)
• Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free [81.65559031466452]
  We conduct experiments to investigate gating-augmented softmax attention variants.<n>We find that a simple modification-applying a head-specific sigmoid gate after the Scaled Dot-Product Attention (SDPA)-consistently improves performance.
  arXiv  Detail & Related papers  (2025-05-10T17:15:49Z)
• Robust Duality Learning for Unsupervised Visible-Infrared Person Re-Identification [24.24793934981947]
  We introduce a new learning paradigm that considers Pseudo-Label Noise (PLN)<n>PLN is characterized by three key challenges: noise overfitting, error accumulation, and noisy cluster correspondence.<n>We propose a novel Robust Duality Learning framework (RoDE) for UVI-ReID to mitigate the effects of noisy pseudo-labels.
  arXiv  Detail & Related papers  (2025-05-05T10:36:52Z)
• UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning [57.081646768835704]
  User specifications or legal frameworks often require information to be removed from pretrained models, including large language models (LLMs)<n>This requires deleting or "forgetting" a set of data points from an already-trained model, which typically degrades its performance on other data points.<n>We propose UPCORE, a method-agnostic data selection framework for mitigating collateral damage during unlearning.
  arXiv  Detail & Related papers  (2025-02-20T22:51:10Z)
• Dual Compensation Residual Networks for Class Imbalanced Learning [98.35401757647749]
  We propose Dual Compensation Residual Networks to better fit both tail and head classes. An important factor causing overfitting is that there is severe feature drift between training and test data on tail classes. We also propose a Residual Balanced Multi-Proxies classifier to alleviate the under-fitting issue.
  arXiv  Detail & Related papers  (2023-08-25T04:06:30Z)
• Anomaly Detection via Multi-Scale Contrasted Memory [3.0170109896527086]
  We introduce a new two-stage anomaly detector which memorizes during training multi-scale normal prototypes to compute an anomaly deviation score. Our model highly improves the state-of-the-art performance on a wide range of object, style and local anomalies with up to 35% error relative improvement on CIFAR-10.
  arXiv  Detail & Related papers  (2022-11-16T16:58:04Z)
• Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification [93.2334223970488]
  We propose two regularizers to prevent hypersphere collapse in deep SVDD. The first regularizer is based on injecting random noise via the standard cross-entropy loss. The second regularizer penalizes the minibatch variance when it becomes too small.
  arXiv  Detail & Related papers  (2020-01-24T03:44:47Z)

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.