Semi-Supervised Teacher-Reference-Student Architecture for Action Quality Assessment

• URL: http://arxiv.org/abs/2407.19675v1
• Date: Mon, 29 Jul 2024 03:36:39 GMT
• Title: Semi-Supervised Teacher-Reference-Student Architecture for Action Quality Assessment
• Authors: Wulian Yun, Mengshi Qi, Fei Peng, Huadong Ma,
• Abstract summary: Action quality assessment (AQA) methods often require a large number of label annotations for fully supervised learning. We propose a novel semi-supervised method, which can be utilized for better assessment of the AQA task by exploiting a large amount of unlabeled data.
• Score: 33.01458098153753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing action quality assessment (AQA) methods often require a large number of label annotations for fully supervised learning, which are laborious and expensive. In practice, the labeled data are difficult to obtain because the AQA annotation process requires domain-specific expertise. In this paper, we propose a novel semi-supervised method, which can be utilized for better assessment of the AQA task by exploiting a large amount of unlabeled data and a small portion of labeled data. Differing from the traditional teacher-student network, we propose a teacher-reference-student architecture to learn both unlabeled and labeled data, where the teacher network and the reference network are used to generate pseudo-labels for unlabeled data to supervise the student network. Specifically, the teacher predicts pseudo-labels by capturing high-level features of unlabeled data. The reference network provides adequate supervision of the student network by referring to additional action information. Moreover, we introduce confidence memory to improve the reliability of pseudo-labels by storing the most accurate ever output of the teacher network and reference network. To validate our method, we conduct extensive experiments on three AQA benchmark datasets. Experimental results show that our method achieves significant improvements and outperforms existing semi-supervised AQA methods.

Related papers

• Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection [134.05510658882278]
  Cyclic-Bootstrap Labeling (CBL) is a novel weakly supervised object detection pipeline. Uses a weighted exponential moving average strategy to take advantage of various refinement modules. A novel class-specific ranking distillation algorithm is proposed to leverage the output of weighted ensembled teacher network.
  arXiv  Detail & Related papers  (2023-08-11T07:57:17Z)
• Active Teacher for Semi-Supervised Object Detection [80.10937030195228]
  We propose a novel algorithm called Active Teacher for semi-supervised object detection (SSOD) Active Teacher extends the teacher-student framework to an iterative version, where the label set is partially and gradually augmented by evaluating three key factors of unlabeled examples. With this design, Active Teacher can maximize the effect of limited label information while improving the quality of pseudo-labels.
  arXiv  Detail & Related papers  (2023-03-15T03:59:27Z)
• 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)
• A Semi-Supervised Learning Approach with Two Teachers to Improve Breakdown Identification in Dialogues [25.499578161686355]
  We propose a novel semi-supervised teacher-student learning framework to tackle this task. We introduce two teachers which are trained on labeled data and perturbed labeled data respectively. We leverage unlabeled data to improve classification in student training where we employ two teachers to refine the labeling of unlabeled data.
  arXiv  Detail & Related papers  (2022-02-22T14:39:51Z)
• QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction [57.56700153507383]
  This paper proposes a unified query attribute value extraction system in e-commerce search named QUEACO. For the NER phase, QUEACO adopts a novel teacher-student network, where a teacher network that is trained on the strongly-labeled data generates pseudo-labels. For the AVN phase, we also leverage the weakly-labeled query-to-attribute behavior data to normalize surface form attribute values from queries into canonical forms from products.
  arXiv  Detail & Related papers  (2021-08-19T03:24:23Z)
• SLADE: A Self-Training Framework For Distance Metric Learning [75.54078592084217]
  We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings.
  arXiv  Detail & Related papers  (2020-11-20T08:26:10Z)
• Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education [1.215785021723604]
  The effort required to develop an educational scenario specific conversational agent is time consuming. Previous approaches to modeling annotations have relied on labeling thousands of examples and calculating inter-annotator agreement and majority votes. We propose using a multi-task weak supervision method combined with active learning to address these concerns.
  arXiv  Detail & Related papers  (2020-10-23T23:39:40Z)
• Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation [65.81546955181781]
  We propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher. The student model learns the knowledge of unlabeled target data and labeled source data by two teacher models. We demonstrate that our approach is able to concurrently utilize unlabeled data and cross-modality data with superior performance.
  arXiv  Detail & Related papers  (2020-07-13T10:00:44Z)

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.