Zobrazeno 1 - 10
of 18 059
pro vyhledávání: '"JIN, XIN"'
Autor:
Xu, Liang, Hua, Shaoyang, Lin, Zili, Liu, Yifan, Ma, Feipeng, Yan, Yichao, Jin, Xin, Yang, Xiaokang, Zeng, Wenjun
In this paper, we tackle the problem of how to build and benchmark a large motion model (LMM). The ultimate goal of LMM is to serve as a foundation model for versatile motion-related tasks, e.g., human motion generation, with interpretability and gen
Externí odkaz:
http://arxiv.org/abs/2410.13790
Since the development of photography art, many so-called "templates" have been formed, namely visual styles summarized from a series of themed and stylized photography works. In this paper, we propose to analysize and and summarize these 'templates'
Externí odkaz:
http://arxiv.org/abs/2410.06124
Training visual reinforcement learning agents in a high-dimensional open world presents significant challenges. While various model-based methods have improved sample efficiency by learning interactive world models, these agents tend to be "short-sig
Externí odkaz:
http://arxiv.org/abs/2410.03618
Autor:
Wang, Yunnan, Li, Ziqiang, Zhang, Zequn, Zhang, Wenyao, Xie, Baao, Liu, Xihui, Zeng, Wenjun, Jin, Xin
There has been exciting progress in generating images from natural language or layout conditions. However, these methods struggle to faithfully reproduce complex scenes due to the insufficient modeling of multiple objects and their relationships. To
Externí odkaz:
http://arxiv.org/abs/2410.00447
Eye movement biometrics has received increasing attention thanks to its high secure identification. Although deep learning (DL) models have been recently successfully applied for eye movement recognition, the DL architecture still is determined by hu
Externí odkaz:
http://arxiv.org/abs/2409.14432
Navigation route recommendation is one of the important functions of intelligent transportation. However, users frequently deviate from recommended routes for various reasons, with personalization being a key problem in the field of research. This pa
Externí odkaz:
http://arxiv.org/abs/2409.14047
Autor:
Zhong, Yinmin, Zhang, Zili, Wu, Bingyang, Liu, Shengyu, Chen, Yukun, Wan, Changyi, Hu, Hanpeng, Xia, Lei, Ming, Ranchen, Zhu, Yibo, Jin, Xin
Reinforcement Learning from Human Feedback (RLHF) enhances the alignment between LLMs and human preference. The workflow of RLHF typically involves several models and tasks in a series of distinct stages. Existing RLHF training systems view each task
Externí odkaz:
http://arxiv.org/abs/2409.13221
Eye movement biometrics is a secure and innovative identification method. Deep learning methods have shown good performance, but their network architecture relies on manual design and combined priori knowledge. To address these issues, we introduce a
Externí odkaz:
http://arxiv.org/abs/2409.11652
In spite of great success in many image recognition tasks achieved by recent deep models, directly applying them to recognize low-resolution images may suffer from low accuracy due to the missing of informative details during resolution degradation.
Externí odkaz:
http://arxiv.org/abs/2409.05384
Autor:
Jin, Xin, Zhu, Hongyu, Li, Siyuan, Wang, Zedong, Liu, Zicheng, Yu, Chang, Qin, Huafeng, Li, Stan Z.
As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive labeled data are unavailable. Among existing augmentations, Mixup and re
Externí odkaz:
http://arxiv.org/abs/2409.05202