Zobrazeno 1 - 10
of 3 651
pro vyhledávání: '"Zhao, Hao"'
In this paper, we study multi-target domain adaptation of scene understanding models. While previous methods achieved commendable results through inter-domain consistency losses, they often assumed unrealistic simultaneous access to images from all t
Externí odkaz:
http://arxiv.org/abs/2407.13771
Fairness is an important topic for medical image analysis, driven by the challenge of unbalanced training data among diverse target groups and the societal demand for equitable medical quality. In response to this issue, our research adopts a data-dr
Externí odkaz:
http://arxiv.org/abs/2407.06250
Fine-tuning large pre-trained foundation models, such as the 175B GPT-3, has attracted more attention for downstream tasks recently. While parameter-efficient fine-tuning methods have been proposed and proven effective without retraining all model pa
Externí odkaz:
http://arxiv.org/abs/2407.01320
In-context learning (ICL) allows LLMs to learn from examples without changing their weights, which is a particularly promising capability for long-context LLMs that can potentially learn from many examples. Recently, Lin et al. (2024) proposed URIAL,
Externí odkaz:
http://arxiv.org/abs/2405.19874
Autor:
Xu, Shiyao, Liu, Caiyun, Chen, Yuantao, Zhu, Zhenxin, Yan, Zike, Shi, Yongliang, Zhao, Hao, Zhou, Guyue
Camera relocalization is a crucial problem in computer vision and robotics. Recent advancements in neural radiance fields (NeRFs) have shown promise in synthesizing photo-realistic images. Several works have utilized NeRFs for refining camera poses,
Externí odkaz:
http://arxiv.org/abs/2405.14824
Autor:
Zhao, Hao, Zhu, Rui-Dong
The connection formulae provide a systematic way to compute physical quantities, such as the quasinormal modes, Green functions, in blackhole perturbation theories. In this work, we test whether it is possible to consistently take the collision limit
Externí odkaz:
http://arxiv.org/abs/2405.03564
Autor:
Ye, Baijun, Liu, Caiyun, Ye, Xiaoyu, Chen, Yuantao, Wang, Yuhai, Yan, Zike, Shi, Yongliang, Zhao, Hao, Zhou, Guyue
Due to the limited model capacity, leveraging distributed Neural Radiance Fields (NeRFs) for modeling extensive urban environments has become a necessity. However, current distributed NeRF registration approaches encounter aliasing artifacts, arising
Externí odkaz:
http://arxiv.org/abs/2405.02880
Autor:
Liu, Junchen, Hu, Wenbo, Yang, Zhuo, Chen, Jianteng, Wang, Guoliang, Chen, Xiaoxue, Cai, Yantong, Gao, Huan-ang, Zhao, Hao
Despite significant advancements in Neural Radiance Fields (NeRFs), the renderings may still suffer from aliasing and blurring artifacts, since it remains a fundamental challenge to effectively and efficiently characterize anisotropic areas induced b
Externí odkaz:
http://arxiv.org/abs/2405.02386
Autor:
Yang, Runyi, Zhu, Zhenxin, Jiang, Zhou, Ye, Baijun, Chen, Xiaoxue, Zhang, Yifei, Chen, Yuantao, Zhao, Jian, Zhao, Hao
Recently, 3D Gaussian Splatting, as a novel 3D representation, has garnered attention for its fast rendering speed and high rendering quality. However, this comes with high memory consumption, e.g., a well-trained Gaussian field may utilize three mil
Externí odkaz:
http://arxiv.org/abs/2405.00676
The intermolecular potential plays crucial roles in real-fluid interactions away from the ideal-gas equilibrium, such as supercritical fluid, high-enthalpy fluid, plasma interactions, etc. We propose a Boltzmann-weighted Full-dimensional (BWF) potent
Externí odkaz:
http://arxiv.org/abs/2404.18700