随着人们对数据隐私保护的迫切需要,近年来,无源目标检测(source - free Object Detection, SFOD)作为数据保护检测的一个新兴分支应运而生。由于目标检测任务的复杂性(多区域、多尺度特征和复杂的网络结构)和缺乏源数据的挑战性,简单地将现有的udaclclassification或UDAOD方法应用于SFOD任务并不能得到满意的结果[48,26...
This study focuses on source-free object detection (SFOD), which adapts a source-trained detector to an unlabeled target domain without using labeled source data. Recent advancements in self-training, particularly with the Mean Teacher (MT) framework, show promise for SFOD deployment. However, the...
UDA方法旨在通过对齐源域和目标域之间检测器模型的特征分布来最小化域差。为了执行特征对齐,UDA方法需要同时访问已标记的源数据和未标记的目标数据。然而,在实际场景中,由于与隐私/安全、数据传输、私有数据等相关的考虑,对源域数据的访问经常受到限制。同时考虑在大规模源域数据上训练的检测模型,当部署在具有不同视觉...
Trinh Le Ba Khanh, Huy-Hung Nguyen, Long Hoang Pham, Duong Nguyen-Ngoc Tran and Jae Wook Jeon Official Pytorch implementation ofDynamic Retraining-Updating Mean Teacher for Source-Free Object Detection, ECCV 2024paper. The overview of our DRU method is presented in the following figure. For mor...
Source-Free Domain Adaptation for YOLO Object Detection Source-free domain adaptation (SFDA) 是一种迁移学习方法,旨在利用源域(source domain)的模型,在目标域(target domain)上进行推理,而无需访问源域的数据。对于YOLO(You Only Look Once)对象检测算法来说,SFDA可以提高模型在不同数据集或环境下的泛化能力。
We investigate the problem of source-free domain adaptation for object detection and identify some of the major challenges that need to be addressed. We introduced an Instance Relation Graph (IRG) framework to model the relationship between proposals generated by the region proposal network. We ...
论文标题:Refined Pseudo labeling for Source-free Domain Adaptive Object Detection论文作者:Siqi Zhang, Lu Zhang, Zhiyong Liu论文来源:2023 ArXiv论文地址:download 论文代码:download视屏讲解:click 1 介绍领域自适应目标检测(DAOD)假设带标记的源数据和未标记的目标数据都可以用于训练,但这种假设在现实世界中并...
Table 6: Impact of Feature-Alignment Strategies on DetectionAccuracy. ThemAPfor the C2F scenario and the carAPfor the K2C and S2C scenarios are reported. The "Source only" method refers to the source model without adaptation. AA stands for Adversarial Alignment and GWA for GW Alignment. ...
such as intel-ligent surveillance systems and automated driving vehicles.Video object detection (VOD) aims to predict the boundingbox and category information of all targeting objects in allvideo frames. Compared to single-frame object detectiontasks, VOD enjoys the advantage of accessing additionalinfor...
A free lunch for unsuper- vised domain adaptive object detection without source data. In AAAI, 2021. 3 [24] Jian Liang, Dapeng Hu, and Jiashi Feng. Do we really need to access the source data? source hypothesis transfer for un- supervised domain...