Zobrazeno 1 - 10
of 2 435
pro vyhledávání: '"LI, YUNFEI"'
Autor:
Yu, Qifan, Shen, Zhebei, Yue, Zhongqi, Wu, Yang, Zhang, Wenqiao, Li, Yunfei, Li, Juncheng, Tang, Siliang, Zhuang, Yueting
Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks. However, the rapid expansion of visual instruction datasets introduces data redundancy, leading to excessive computational costs. We propo
Externí odkaz:
http://arxiv.org/abs/2412.06293
Autor:
Gao, Minghe, Bu, Wendong, Miao, Bingchen, Wu, Yang, Li, Yunfei, Li, Juncheng, Tang, Siliang, Wu, Qi, Zhuang, Yueting, Wang, Meng
In this paper, we introduce the Generalist Virtual Agent (GVA), an autonomous entity engineered to function across diverse digital platforms and environments, assisting users by executing a variety of tasks. This survey delves into the evolution of G
Externí odkaz:
http://arxiv.org/abs/2411.10943
Autor:
Li, Shuzhen, Chen, Yuxin, Chen, Xuesong, Gao, Ruiyang, Zhang, Yupeng, Yu, Chao, Li, Yunfei, Ye, Ziyi, Huang, Weijun, Yi, Hongliang, Leng, Yue, Wu, Yi
Sleep monitoring plays a crucial role in maintaining good health, with sleep staging serving as an essential metric in the monitoring process. Traditional methods, utilizing medical sensors like EEG and ECG, can be effective but often present challen
Externí odkaz:
http://arxiv.org/abs/2410.22646
Real-world DeepFake videos often undergo various compression operations, resulting in a range of video qualities. These varying qualities diversify the pattern of forgery traces, significantly increasing the difficulty of DeepFake detection. To addre
Externí odkaz:
http://arxiv.org/abs/2410.07633
Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information. Research in this field generally consist of two main perspectives: local-level enco
Externí odkaz:
http://arxiv.org/abs/2406.12608
Sequential DeepFake detection is an emerging task that predicts the manipulation sequence in order. Existing methods typically formulate it as an image-to-sequence problem, employing conventional Transformer architectures. However, these methods lack
Externí odkaz:
http://arxiv.org/abs/2404.13873
This letter proposes a novel hybrid automatic repeat request with chase combining assisted sparse code multiple access (HARQ-CC-SCMA) scheme. Depending on whether the same superimposed packet are retransmitted, synchronous and asynchronous modes are
Externí odkaz:
http://arxiv.org/abs/2404.09814
We present a large language model (LLM) based system to empower quadrupedal robots with problem-solving abilities for long-horizon tasks beyond short-term motions. Long-horizon tasks for quadrupeds are challenging since they require both a high-level
Externí odkaz:
http://arxiv.org/abs/2404.05291
Autor:
Su, Zhi, Huang, Xiaoyu, Ordoñez-Apraez, Daniel, Li, Yunfei, Li, Zhongyu, Liao, Qiayuan, Turrisi, Giulio, Pontil, Massimiliano, Semini, Claudio, Wu, Yi, Sreenath, Koushil
Model-free reinforcement learning is a promising approach for autonomously solving challenging robotics control problems, but faces exploration difficulty without information of the robot's kinematics and dynamics morphology. The under-exploration of
Externí odkaz:
http://arxiv.org/abs/2403.17320
This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distance-dependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we c
Externí odkaz:
http://arxiv.org/abs/2403.03809