Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Chen, Zhuoxiao"'
Autor:
Lim, Jia Syuen, Chen, Zhuoxiao, Baktashmotlagh, Mahsa, Chen, Zhi, Yu, Xin, Huang, Zi, Luo, Yadan
Class-agnostic object detection (OD) can be a cornerstone or a bottleneck for many downstream vision tasks. Despite considerable advancements in bottom-up and multi-object discovery methods that leverage basic visual cues to identify salient objects,
Externí odkaz:
http://arxiv.org/abs/2406.14924
LiDAR-based 3D object detection is crucial for various applications but often experiences performance degradation in real-world deployments due to domain shifts. While most studies focus on cross-dataset shifts, such as changes in environments and ob
Externí odkaz:
http://arxiv.org/abs/2406.14878
LiDAR-based 3D object detection has seen impressive advances in recent times. However, deploying trained 3D detectors in the real world often yields unsatisfactory performance when the distribution of the test data significantly deviates from the tra
Externí odkaz:
http://arxiv.org/abs/2406.13891
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. Despite the recent proliferation of OTTA method
Externí odkaz:
http://arxiv.org/abs/2310.20199
LiDAR-based 3D object detection has recently seen significant advancements through active learning (AL), attaining satisfactory performance by training on a small fraction of strategically selected point clouds. However, in real-world deployments whe
Externí odkaz:
http://arxiv.org/abs/2310.10391
Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection. While effective, existing DA methods suffer from a substantial drop in performance when applied
Externí odkaz:
http://arxiv.org/abs/2307.07944
Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations. Active learning (AL) seeks to mitigate the annotation burden through algorithms that use
Externí odkaz:
http://arxiv.org/abs/2307.07942
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. Our empirical study, h
Externí odkaz:
http://arxiv.org/abs/2301.09249
Open-set domain adaptation (OSDA) has gained considerable attention in many visual recognition tasks. However, most existing OSDA approaches are limited due to three main reasons, including: (1) the lack of essential theoretical analysis of generaliz
Externí odkaz:
http://arxiv.org/abs/2202.06174
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. This can effectively address the issue of an expensive and time-consumin
Externí odkaz:
http://arxiv.org/abs/2109.03385