Versatile Teacher: A Class-aware Teacher-student Framework for Cross-domain Adaptation

• URL: http://arxiv.org/abs/2405.11754v1
• Date: Mon, 20 May 2024 03:31:43 GMT
• Title: Versatile Teacher: A Class-aware Teacher-student Framework for Cross-domain Adaptation
• Authors: Runou Yang, Tian Tian, Jinwen Tian,
• Abstract summary: We introduce a novel teacher-student model named Versatile Teacher (VT) VT considers class-specific detection difficulty and employs a two-step pseudo-label selection mechanism to generate more reliable pseudo labels. Our method demonstrates promising results on three benchmark datasets, and extends the alignment methods for widely-used one-stage detectors.
• Score: 2.9748058103007957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Addressing the challenge of domain shift between datasets is vital in maintaining model performance. In the context of cross-domain object detection, the teacher-student framework, a widely-used semi-supervised model, has shown significant accuracy improvements. However, existing methods often overlook class differences, treating all classes equally, resulting in suboptimal results. Furthermore, the integration of instance-level alignment with a one-stage detector, essential due to the absence of a Region Proposal Network (RPN), remains unexplored in this framework. In response to these shortcomings, we introduce a novel teacher-student model named Versatile Teacher (VT). VT differs from previous works by considering class-specific detection difficulty and employing a two-step pseudo-label selection mechanism, referred to as Class-aware Pseudo-label Adaptive Selection (CAPS), to generate more reliable pseudo labels. These labels are leveraged as saliency matrices to guide the discriminator for targeted instance-level alignment. Our method demonstrates promising results on three benchmark datasets, and extends the alignment methods for widely-used one-stage detectors, presenting significant potential for practical applications. Code is available at https://github.com/RicardooYoung/VersatileTeacher.

Related papers

• Diverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch [35.42630035488178]
  We introduce a novel framework named Diverse Teacher-Students (textbfDTS) By training both teacher-student models with all unlabeled samples, DTS can enhance the classification of seen classes while simultaneously improving the detection of unseen classes.
  arXiv  Detail & Related papers  (2024-05-25T06:54:43Z)
• Unsupervised Domain Adaptation for Semantic Segmentation with Pseudo Label Self-Refinement [9.69089112870202]
  We propose an auxiliary pseudo-label refinement network (PRN) for online refining of the pseudo labels and also localizing the pixels whose predicted labels are likely to be noisy. We evaluate our approach on benchmark datasets with three different domain shifts, and our approach consistently performs significantly better than the previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-25T20:31:07Z)
• Semi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty [46.21528260727673]
  We propose a two-step pseudo-label filtering for the classification and regression heads in a teacher-student framework. By jointly filtering the pseudo-labels for the classification and regression heads, the student network receives better guidance from the teacher network for object detection task.
  arXiv  Detail & Related papers  (2022-12-06T04:37:51Z)
• 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)
• UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation [84.16372642822495]
  We propose an unsupervised domain adaptation (UDA) for category-level object pose estimation, called textbfUDA-COPE. Inspired by the recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain labels.
  arXiv  Detail & Related papers  (2021-11-24T16:00:48Z)
• On Universal Black-Box Domain Adaptation [53.7611757926922]
  We study an arguably least restrictive setting of domain adaptation in a sense of practical deployment. Only the interface of source model is available to the target domain, and where the label-space relations between the two domains are allowed to be different and unknown. We propose to unify them into a self-training framework, regularized by consistency of predictions in local neighborhoods of target samples.
  arXiv  Detail & Related papers  (2021-04-10T02:21:09Z)
• 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)
• Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification [64.37745443119942]
  This paper jointly enforces visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification. Experimental results on three large-scale ReID datasets demonstrate the superiority of proposed method in both unsupervised and unsupervised domain adaptive ReID tasks.
  arXiv  Detail & Related papers  (2020-07-21T14:31:27Z)
• Learning a Domain Classifier Bank for Unsupervised Adaptive Object Detection [48.19258721979389]
  In this paper, we propose a fine-grained domain alignment approach for object detectors based on deep networks. We develop a bare object detector with the proposed fine-grained domain alignment mechanism as the adaptive detector. Experiments on three popular transferring benchmarks demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2020-07-06T09:12:46Z)

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.