Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation

• URL: http://arxiv.org/abs/2311.01770v1
• Date: Fri, 3 Nov 2023 08:11:06 GMT
• Title: Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation
• Authors: Jiaqi Wu, Junbiao Pang, Qingming Huang
• Abstract summary: We propose a framework to estimate the quality of pseudo-labels in semi-supervised pose estimation tasks. Our method improves the performance of semi-supervised pose estimation on three datasets.
• Score: 57.17120203327993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised pose estimation is a practically challenging task for computer vision. Although numerous excellent semi-supervised classification methods have emerged, these methods typically use confidence to evaluate the quality of pseudo-labels, which is difficult to achieve in pose estimation tasks. For example, in pose estimation, confidence represents only the possibility that a position of the heatmap is a keypoint, not the quality of that prediction. In this paper, we propose a simple yet efficient framework to estimate the quality of pseudo-labels in semi-supervised pose estimation tasks from the perspective of modeling the uncertainty of the pseudo-labels. Concretely, under the dual mean-teacher framework, we construct the two maximum discrepant students (MDSs) to effectively push two teachers to generate different decision boundaries for the same sample. Moreover, we create multiple uncertainties to assess the quality of the pseudo-labels. Experimental results demonstrate that our method improves the performance of semi-supervised pose estimation on three datasets.

Related papers

• TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection [59.498894868956306]
  Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student framework. We leverage pre-trained motion-forecasting models to generate object trajectories on pseudo-labeled data. Our approach improves pseudo-label quality in two distinct manners.
  arXiv  Detail & Related papers  (2024-09-17T05:35:00Z)
• Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning [81.83013974171364]
  Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance. We propose a dual-perspective method to generate high-quality pseudo-labels.
  arXiv  Detail & Related papers  (2024-07-26T09:33:53Z)
• Semi-supervised 2D Human Pose Estimation via Adaptive Keypoint Masking [2.297586471170049]
  This paper proposes an adaptive keypoint masking method, which can fully mine the information in the samples and obtain better estimation performance. The effectiveness of the proposed method is verified on COCO and MPII, outperforming the state-of-the-art semi-supervised pose estimation by 5.2% and 0.3%, respectively.
  arXiv  Detail & Related papers  (2024-04-23T08:41:50Z)
• Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection [38.77989138502667]
  Semi-supervised 3D object detection from point cloud aims to train a detector with a small number of labeled data and a large number of unlabeled data. Existing methods treat each pseudo bounding box as a whole and assign equal importance to each side during training. We propose a side-aware framework for semi-supervised 3D object detection consisting of three key designs.
  arXiv  Detail & Related papers  (2023-12-16T09:08:03Z)
• Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation [38.97427474379367]
  We introduce a denoising scheme to generate reliable pseudo-heatmaps as targets for learning from unlabeled data. We select the learning targets from these pseudo-heatmaps guided by the estimated cross-student uncertainty. Our results show that our model outperforms previous state-of-the-art semi-supervised pose estimators.
  arXiv  Detail & Related papers  (2023-09-29T19:17:30Z)
• Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module [74.80776648785897]
  The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models. We propose a semi-supervised 2D human pose estimation framework driven by a position inconsistency pseudo label correction module (SSPCM) To further improve the performance of the student model, we use the semi-supervised Cut-Occlude based on pseudo keypoint perception to generate more hard and effective samples.
  arXiv  Detail & Related papers  (2023-03-08T02:57:05Z)
• Uncertain Facial Expression Recognition via Multi-task Assisted Correction [43.02119884581332]
  We propose a novel method of multi-task assisted correction in addressing uncertain facial expression recognition called MTAC. Specifically, a confidence estimation block and a weighted regularization module are applied to highlight solid samples and suppress uncertain samples in every batch. Experiments on RAF-DB, AffectNet, and AffWild2 datasets demonstrate that the MTAC obtains substantial improvements over baselines when facing synthetic and real uncertainties.
  arXiv  Detail & Related papers  (2022-12-14T10:28:08Z)
• Label Matching Semi-Supervised Object Detection [85.99282969977541]
  Semi-supervised object detection has made significant progress with the development of mean teacher driven self-training. Label mismatch problem is not yet fully explored in the previous works, leading to severe confirmation bias during self-training. We propose a simple yet effective LabelMatch framework from two different yet complementary perspectives.
  arXiv  Detail & Related papers  (2022-06-14T05:59:41Z)
• Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin [92.76372026435858]
  We learn an Adaptive Confidence Margin (Ada-CM) to fully leverage all unlabeled data for semi-supervised deep facial expression recognition. All unlabeled samples are partitioned into two subsets by comparing their confidence scores with the adaptively learned confidence margin. Our method achieves state-of-the-art performance, especially surpassing fully-supervised baselines in a semi-supervised manner.
  arXiv  Detail & Related papers  (2022-03-23T11:43:29Z)
• Double-Uncertainty Weighted Method for Semi-supervised Learning [32.484750353853954]
  We propose a double-uncertainty weighted method for semi-supervised segmentation based on the teacher-student model. We train the teacher model using Bayesian deep learning to obtain double-uncertainty, i.e. segmentation uncertainty and feature uncertainty. Our method outperforms the state-of-the-art uncertainty-based semi-supervised methods on two public medical datasets.
  arXiv  Detail & Related papers  (2020-10-19T08:20:18Z)

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.