Knowledge Commons of Institute of Automation,CAS
Multi-Prototype Guided Source-Free Domain Adaptive Object Detection for Autonomous Driving | |
Siqi Zhang![]() ![]() ![]() | |
发表期刊 | https://ieeexplore.ieee.org/document/10336548/
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2024-01 | |
卷号 | 9期号:1页码:1589-1601 |
文章类型 | 期刊论文 |
摘要 | Source-free domain adaptive object detection (source-free DAOD) seeks to adapt a detector pre-trained on a source domain to an unlabeled target domain without requiring access to annotated source domain data. To address challenges posed by domain shifts, current source-free DAOD approaches mainly rely on the self-training paradigm, where pseudo labels are predicted and employed to fine-tune the detector on unlabeled target domain. However, these methods often encounter issues related to intra-class variation, resulting in category-specific biases and noisy pseudo labels. In response, we present an effective Multi-Prototype Guided source-free DAOD method, dubbed MPG, consisting of two key components: multi-prototype guided pseudo labeling (MPPL) and multi-prototype guided consistency regularization (MPCR) modules. In the MPPL module, we construct category-specific multiple prototypes to better represent the category with intra-class variations. Specifically, multiple prototypes with adaptive cluster centroids are introduced for each category to effectively capture the intra-class variations. Through the implementation of the proposed MPPL module, we derive more accurate pseudo labels by assessing the proximity of instance features to multiple category prototypes. In the MPCR module, we introduce multi-level consistency regularization, including prototype-based consistency and prediction consistency, which encourages the model to overlook style perturbations and learn domain-invariant representations. Extensive experiments on five public driving datasets demonstrate that MPG outperforms existing state-of-the-art methods, showcasing its effectiveness in adapting object detectors to target domains. |
语种 | 英语 |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57279 |
专题 | 多模态人工智能系统全国重点实验室 |
作者单位 | State Key Laboratory of Multimodal Artificial In- telligence Systems, Institute of Automation, Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Siqi Zhang,Lu Zhang,Guangsen Li,et al. Multi-Prototype Guided Source-Free Domain Adaptive Object Detection for Autonomous Driving[J]. https://ieeexplore.ieee.org/document/10336548/,2024,9(1):1589-1601. |
APA | Siqi Zhang,Lu Zhang,Guangsen Li,Pengcheng Li,&Zhiyong Liu.(2024).Multi-Prototype Guided Source-Free Domain Adaptive Object Detection for Autonomous Driving.https://ieeexplore.ieee.org/document/10336548/,9(1),1589-1601. |
MLA | Siqi Zhang,et al."Multi-Prototype Guided Source-Free Domain Adaptive Object Detection for Autonomous Driving".https://ieeexplore.ieee.org/document/10336548/ 9.1(2024):1589-1601. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Zhang 等 - 2024 - Mul(4349KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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