Active Teacher for Semi-Supervised Object Detection

• URL: http://arxiv.org/abs/2303.08348v1
• Date: Wed, 15 Mar 2023 03:59:27 GMT
• Title: Active Teacher for Semi-Supervised Object Detection
• Authors: Peng Mi, Jianghang Lin, Yiyi Zhou, Yunhang Shen, Gen Luo, Xiaoshuai Sun, Liujuan Cao, Rongrong Fu, Qiang Xu, Rongrong Ji
• Abstract summary: 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.
• Score: 80.10937030195228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher(Source code are available at: \url{https://github.com/HunterJ-Lin/ActiveTeacher}) for semi-supervised object detection (SSOD). Active Teacher extends the teacher-student framework to an iterative version, where the label set is partially initialized and gradually augmented by evaluating three key factors of unlabeled examples, including difficulty, information and diversity. With this design, Active Teacher can maximize the effect of limited label information while improving the quality of pseudo-labels. To validate our approach, we conduct extensive experiments on the MS-COCO benchmark and compare Active Teacher with a set of recently proposed SSOD methods. The experimental results not only validate the superior performance gain of Active Teacher over the compared methods, but also show that it enables the baseline network, ie, Faster-RCNN, to achieve 100% supervised performance with much less label expenditure, ie 40% labeled examples on MS-COCO. More importantly, we believe that the experimental analyses in this paper can provide useful empirical knowledge for data annotation in practical applications.

Related papers

• Semi-Supervised Teacher-Reference-Student Architecture for Action Quality Assessment [33.01458098153753]
  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.
  arXiv  Detail & Related papers  (2024-07-29T03:36:39Z)
• Make a Strong Teacher with Label Assistance: A Novel Knowledge Distillation Approach for Semantic Segmentation [40.80204896051931]
  We introduce a novel knowledge distillation approach for the semantic segmentation task. For the teacher model training, we propose to noise the label and then incorporate it into input to effectively boost the lightweight teacher performance. Our approach not only boosts the efficacy of knowledge distillation but also increases the flexibility in selecting teacher and student models.
  arXiv  Detail & Related papers  (2024-07-18T08:08:04Z)
• BAL: Balancing Diversity and Novelty for Active Learning [53.289700543331925]
  We introduce a novel framework, Balancing Active Learning (BAL), which constructs adaptive sub-pools to balance diverse and uncertain data. Our approach outperforms all established active learning methods on widely recognized benchmarks by 1.20%.
  arXiv  Detail & Related papers  (2023-12-26T08:14:46Z)
• Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection [90.32180043449263]
  State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations. A natural remedy is to adopt semi-supervised learning (SSL) by leveraging a limited amount of labeled samples and abundant unlabeled samples. This paper introduces a novel approach of Hierarchical Supervision and Shuffle Data Augmentation (HSSDA), which is a simple yet effective teacher-student framework.
  arXiv  Detail & Related papers  (2023-04-04T02:09:32Z)
• Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models [10.941519846908697]
  We introduce a new type of teacher model for connectionist temporal classification ( CTC)-based sequence models, namely Oracle Teacher. Since the Oracle Teacher learns a more accurate CTC alignment by referring to the target information, it can provide the student with more optimal guidance. Based on a many-to-one mapping property of the CTC algorithm, we present a training strategy that can effectively prevent the trivial solution.
  arXiv  Detail & Related papers  (2021-11-05T14:14:05Z)
• Graph Consistency based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification [54.58165777717885]
  This paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with constructing the Graph Consistency Constraint (GCC) between teacher and student networks. Experiments on three datasets, i.e., Market-1501, DukeMTMCreID, and MSMT17, show that proposed GCMT outperforms state-of-the-art methods by clear margin.
  arXiv  Detail & Related papers  (2021-05-11T04:09:49Z)
• Unbiased Teacher for Semi-Supervised Object Detection [50.0087227400306]
  We revisit the Semi-Supervised Object Detection (SS-OD) and identify the pseudo-labeling bias issue in SS-OD. We introduce Unbiased Teacher, a simple yet effective approach that jointly trains a student and a gradually progressing teacher in a mutually-beneficial manner.
  arXiv  Detail & Related papers  (2021-02-18T17:02:57Z)
• 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)

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.