Zobrazeno 1 - 10
of 10 970
pro vyhledávání: '"Zhao, Xu"'
Autor:
Cai, Chengkun, Zhao, Xu, Liu, Haoliang, Jiang, Zhongyu, Zhang, Tianfang, Wu, Zongkai, Hwang, Jenq-Neng, Li, Lei
Large Language Models (LLMs) have achieved substantial progress in artificial intelligence, particularly in reasoning tasks. However, their reliance on static prompt structures, coupled with limited dynamic reasoning capabilities, often constrains th
Externí odkaz:
http://arxiv.org/abs/2410.02892
Autor:
Zhang, Yesheng, Zhao, Xu
We propose MESA and DMESA as novel feature matching methods, which utilize Segment Anything Model (SAM) to effectively mitigate matching redundancy. The key insight of our methods is to establish implicit-semantic area matching prior to point matchin
Externí odkaz:
http://arxiv.org/abs/2408.00279
Epoch extraction has become increasingly popular in recent years for speech analysis research because accurately detecting the location of the Epoch is crucial for analyzing speech signals. The Epoch, occurring at the instant of excitation in the voc
Externí odkaz:
http://arxiv.org/abs/2407.18447
Large Language Models (LLMs) have emerged as powerful tools in artificial intelligence, especially in complex decision-making scenarios, but their static problem-solving strategies often limit their adaptability to dynamic environments. We explore th
Externí odkaz:
http://arxiv.org/abs/2405.14075
Temporal 3D human pose estimation from monocular videos is a challenging task in human-centered computer vision due to the depth ambiguity of 2D-to-3D lifting. To improve accuracy and address occlusion issues, inertial sensor has been introduced to p
Externí odkaz:
http://arxiv.org/abs/2404.17837
To reconstruct a 3D human surface from a single image, it is crucial to simultaneously consider human pose, shape, and clothing details. Recent approaches have combined parametric body models (such as SMPL), which capture body pose and shape priors,
Externí odkaz:
http://arxiv.org/abs/2401.16810
Autor:
Zhang, Yesheng, Zhao, Xu
Feature matching is a crucial task in the field of computer vision, which involves finding correspondences between images. Previous studies achieve remarkable performance using learning-based feature comparison. However, the pervasive presence of mat
Externí odkaz:
http://arxiv.org/abs/2401.16741
Network Pruning is a promising way to address the huge computing resource demands of the deployment and inference of Large Language Models (LLMs). Retraining-free is important for LLMs' pruning methods. However, almost all of the existing retraining-
Externí odkaz:
http://arxiv.org/abs/2312.11983
In the domain of 3D Human Pose Estimation, which finds widespread daily applications, the requirement for convenient acquisition equipment continues to grow. To satisfy this demand, we set our sights on a short-baseline binocular setting that offers
Externí odkaz:
http://arxiv.org/abs/2311.14242
Autor:
Li, Kaixin, Hu, Qisheng, Zhao, Xu, Chen, Hui, Xie, Yuxi, Liu, Tiedong, Xie, Qizhe, He, Junxian
Code editing encompasses a variety of pragmatic tasks that developers deal with daily. Despite its relevance and practical usefulness, automatic code editing remains an underexplored area in the evolution of deep learning models, partly due to data s
Externí odkaz:
http://arxiv.org/abs/2310.20329