Zobrazeno 1 - 10
of 318
pro vyhledávání: '"Huang, Kuan‐Chih"'
Recent advancements in multimodal large language models (LLMs) have shown their potential in various domains, especially concept reasoning. Despite these developments, applications in understanding 3D environments remain limited. This paper introduce
Externí odkaz:
http://arxiv.org/abs/2405.17427
Recent temporal LiDAR-based 3D object detectors achieve promising performance based on the two-stage proposal-based approach. They generate 3D box candidates from the first-stage dense detector, followed by different temporal aggregation methods. How
Externí odkaz:
http://arxiv.org/abs/2312.08371
Weakly supervised 3D object detection aims to learn a 3D detector with lower annotation cost, e.g., 2D labels. Unlike prior work which still relies on few accurate 3D annotations, we propose a framework to study how to leverage constraints between 2D
Externí odkaz:
http://arxiv.org/abs/2312.07530
Recent advances of monocular 3D object detection facilitate the 3D multi-object tracking task based on low-cost camera sensors. In this paper, we find that the motion cue of objects along different time frames is critical in 3D multi-object tracking,
Externí odkaz:
http://arxiv.org/abs/2308.11607
Autor:
Lai, Wei-Tsung, Chen, I-Chen, Hsiung, Ming-Chon, Lin, Ting-Chao, Huang, Kuan-Chih, Chang, Chung-Yi, Wei, Jeng
Publikováno v:
In International Journal of Cardiology Cardiovascular Risk and Prevention December 2024 23
Monocular 3D object detection is an important yet challenging task in autonomous driving. Some existing methods leverage depth information from an off-the-shelf depth estimator to assist 3D detection, but suffer from the additional computational burd
Externí odkaz:
http://arxiv.org/abs/2203.10981
Autor:
Wu, Tsung-Han, Liou, Yi-Syuan, Yuan, Shao-Ji, Lee, Hsin-Ying, Chen, Tung-I, Huang, Kuan-Chih, Hsu, Winston H.
In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with few informative labeled data, comes in handy for such a scenario. In t
Externí odkaz:
http://arxiv.org/abs/2202.06484
Publikováno v:
Bayesian Deep Learning Workshop, NeurIPS 2021
Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift is a competition held by NeurIPS 2021. The objective of this competition is to search for methods to solve the motion prediction problem in cross-domain. In the real w
Externí odkaz:
http://arxiv.org/abs/2112.01348
Multi-Stream Attention Learning for Monocular Vehicle Velocity and Inter-Vehicle Distance Estimation
Vehicle velocity and inter-vehicle distance estimation are essential for ADAS (Advanced driver-assistance systems) and autonomous vehicles. To save the cost of expensive ranging sensors, recent studies focus on using a low-cost monocular camera to pe
Externí odkaz:
http://arxiv.org/abs/2110.11608
Publikováno v:
In Mayo Clinic Proceedings September 2024 99(9):1374-1387