1 code implementation • 21 Jun 2024 • Jia Syuen Lim, Zhuoxiao Chen, Mahsa Baktashmotlagh, Zhi Chen, Xin Yu, Zi Huang, Yadan Luo
We demonstrate the effectiveness of DiPEx through extensive class-agnostic OD and OOD-OD experiments on MS-COCO and LVIS, surpassing other prompting methods by up to 20. 1% in AR and achieving a 21. 3% AP improvement over SAM.
no code implementations • 21 Jun 2024 • Zhuoxiao Chen, Junjie Meng, Mahsa Baktashmotlagh, Yonggang Zhang, Zi Huang, Yadan Luo
Specifically, we propose a Model Synergy (MOS) strategy that dynamically selects historical checkpoints with diverse knowledge and assembles them to best accommodate the current test batch.
1 code implementation • 19 Jun 2024 • Zhuoxiao Chen, Zixin Wang, Yadan Luo, Sen Wang, Zi Huang
We minimize the sharpness to cultivate a flat loss landscape to ensure model resiliency to minor data variations, thereby enhancing the generalization of the adaptation process.
1 code implementation • 31 Oct 2023 • Zixin Wang, Yadan Luo, Liang Zheng, Zhuoxiao Chen, Sen Wang, Zi Huang
This article presents a comprehensive survey of online test-time adaptation (OTTA), focusing on effectively adapting machine learning models to distributionally different target data upon batch arrival.
1 code implementation • 16 Oct 2023 • Zhuoxiao Chen, Yadan Luo, Zixin Wang, Zijian Wang, Xin Yu, Zi Huang
This paper investigates a more practical and challenging research task: Open World Active Learning for 3D Object Detection (OWAL-3D), aimed at acquiring informative point clouds with new concepts.
1 code implementation • ICCV 2023 • Zhuoxiao Chen, Yadan Luo, Zheng Wang, Mahsa Baktashmotlagh, Zi Huang
Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection.
no code implementations • ICCV 2023 • Yadan Luo, Zhuoxiao Chen, Zhen Fang, Zheng Zhang, Zi Huang, Mahsa Baktashmotlagh
Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations.
1 code implementation • 23 Jan 2023 • Yadan Luo, Zhuoxiao Chen, Zijian Wang, Xin Yu, Zi Huang, Mahsa Baktashmotlagh
To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance.
2 code implementations • 13 Feb 2022 • Yadan Luo, Zijian Wang, Zhuoxiao Chen, Zi Huang, Mahsa Baktashmotlagh
However, most existing OSDA approaches are limited due to three main reasons, including: (1) the lack of essential theoretical analysis of generalization bound, (2) the reliance on the coexistence of source and target data during adaptation, and (3) failing to accurately estimate the uncertainty of model predictions.
no code implementations • 8 Sep 2021 • Zhuoxiao Chen, Yiyun Zhang, Yadan Luo, Zijian Wang, Jinjiang Zhong, Anthony Southon
With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing.
1 code implementation • 1 Sep 2021 • Zhuoxiao Chen, Yadan Luo, Mahsa Baktashmotlagh
The majority of video domain adaptation algorithms are proposed for closed-set scenarios in which all the classes are shared among the domains.