General Class-Balanced Multicentric Dynamic Prototype Pseudo-Labeling for Source-Free Domain Adaptation

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Computer Vision Pub Date : 2025-01-05 DOI:10.1007/s11263-024-02335-w
Sanqing Qu, Guang Chen, Jing Zhang, Zhijun Li, Wei He, Dacheng Tao
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Abstract

Source-free Domain Adaptation aims to adapt a pre-trained source model to an unlabeled target domain while circumventing access to well-labeled source data. To compensate for the absence of source data, most existing approaches employ prototype-based pseudo-labeling strategies to facilitate self-training model adaptation. Nevertheless, these methods commonly rely on instance-level predictions for direct monocentric prototype construction, leading to category bias and noisy labels. This is primarily due to the inherent visual domain gaps that often differ across categories. Besides, the monocentric prototype design is ineffective and may introduce negative transfer for those ambiguous data. To tackle these challenges, we propose a general class-Balanced Multicentric Dynamic (BMD) prototype strategy. Specifically, we first introduce a global inter-class balanced sampling strategy for each target category to mitigate category bias. Subsequently, we design an intra-class multicentric clustering strategy to generate robust and representative prototypes. In contrast to existing approaches that only update pseudo-labels at fixed intervals, e.g., one epoch, we employ a dynamic pseudo-labeling strategy that incorporates network update information throughout the model adaptation. We refer to the vanilla implementation of these three sub-strategies as BMD-v1. Furthermore, we promote the BMD-v1 to BMD-v2 by incorporating a consistency-guided reweighting strategy to improve inter-class balanced sampling, and leveraging the silhouettes metric to realize adaptive intra-class multicentric clustering. Extensive experiments conducted on both 2D images and 3D point cloud recognition demonstrate that our proposed BMD strategy significantly improves existing representative methods. Remarkably, BMD-v2 improves NRC from 52.6 to 59.2% in accuracy on the PointDA-10 benchmark. The code will be available at https://github.com/ispc-lab/BMD.

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用于无源域自适应的通用类平衡多中心动态原型伪标记
无源域自适应旨在使预训练的源模型适应未标记的目标域,同时绕过对标记良好的源数据的访问。为了弥补源数据的缺失,大多数现有方法采用基于原型的伪标记策略来促进自训练模型的适应。然而,这些方法通常依赖于实例级预测来直接构建单中心原型,从而导致类别偏差和噪声标签。这主要是由于固有的视觉领域差距,往往不同类别。此外,单中心的原型设计是无效的,并且可能对那些模糊的数据引入负迁移。为了应对这些挑战,我们提出了一种通用类平衡多中心动态(BMD)原型策略。具体而言,我们首先为每个目标类别引入全局类间平衡抽样策略以减轻类别偏差。随后,我们设计了一个类内多中心聚类策略来生成鲁棒性和代表性的原型。与仅在固定间隔(例如一个epoch)更新伪标签的现有方法相比,我们采用了一种动态伪标签策略,该策略在整个模型适应过程中包含网络更新信息。我们将这三个子策略的普通实现称为BMD-v1。此外,我们将BMD-v1升级为BMD-v2,通过引入一致性导向的重权策略来改善类间均衡采样,并利用轮廓度量来实现自适应类内多中心聚类。在二维图像和三维点云识别上进行的大量实验表明,我们提出的BMD策略显著改进了现有的代表性方法。值得注意的是,在PointDA-10基准测试中,BMD-v2将NRC的准确率从52.6提高到59.2%。代码可在https://github.com/ispc-lab/BMD上获得。
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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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