Zobrazeno 1 - 10
of 850
pro vyhledávání: '"Chen, Haonan"'
Autor:
Bai, Ye, Chen, Haonan, Chen, Jitong, Chen, Zhuo, Deng, Yi, Dong, Xiaohong, Hantrakul, Lamtharn, Hao, Weituo, Huang, Qingqing, Huang, Zhongyi, Jia, Dongya, La, Feihu, Le, Duc, Li, Bochen, Li, Chumin, Li, Hui, Li, Xingxing, Liu, Shouda, Lu, Wei-Tsung, Lu, Yiqing, Shaw, Andrew, Spijkervet, Janne, Sun, Yakun, Wang, Bo, Wang, Ju-Chiang, Wang, Yuping, Wang, Yuxuan, Xu, Ling, Yang, Yifeng, Yao, Chao, Zhang, Shuo, Zhang, Yang, Zhang, Yilin, Zhao, Hang, Zhao, Ziyi, Zhong, Dejian, Zhou, Shicen, Zou, Pei
We introduce Seed-Music, a suite of music generation systems capable of producing high-quality music with fine-grained style control. Our unified framework leverages both auto-regressive language modeling and diffusion approaches to support two key m
Externí odkaz:
http://arxiv.org/abs/2409.09214
Autor:
Chen, Haonan, Smith, Jordan B. L., Spijkervet, Janne, Wang, Ju-Chiang, Zou, Pei, Li, Bochen, Kong, Qiuqiang, Du, Xingjian
Progress in the task of symbolic music generation may be lagging behind other tasks like audio and text generation, in part because of the scarcity of symbolic training data. In this paper, we leverage the greater scale of audio music data by applyin
Externí odkaz:
http://arxiv.org/abs/2409.03055
With the rapid growth of video data on the internet, video summarization is becoming a very important AI technology. However, due to the high labelling cost of video summarization, existing studies have to be conducted on small-scale datasets, leadin
Externí odkaz:
http://arxiv.org/abs/2408.12800
Modeling contextual information in a search session has drawn more and more attention when understanding complex user intents. Recent methods are all data-driven, i.e., they train different models on large-scale search log data to identify the releva
Externí odkaz:
http://arxiv.org/abs/2407.03720
Autor:
Peng, Weikun, Lv, Jun, Zeng, Yuwei, Chen, Haonan, Zhao, Siheng, Sun, Jichen, Lu, Cewu, Shao, Lin
The tie-knotting task is highly challenging due to the tie's high deformation and long-horizon manipulation actions. This work presents TieBot, a Real-to-Sim-to-Real learning from visual demonstration system for the robots to learn to knot a tie. We
Externí odkaz:
http://arxiv.org/abs/2407.03245
Real-time Advisory (RTA) systems, such as navigational and eco-driving assistants, are becoming increasingly ubiquitous in vehicles due to their benefits for users and society. Until autonomous vehicles mature, such advisory systems will continue to
Externí odkaz:
http://arxiv.org/abs/2407.13775
Autor:
Hasan, Aamir, Chakraborty, Neeloy, Chen, Haonan, Cho, Jung-Hoon, Wu, Cathy, Driggs-Campbell, Katherine
Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these approaches are limited in practice as they assume precise control over
Externí odkaz:
http://arxiv.org/abs/2407.00553
Autor:
He, Xuan, Jiang, Dongfu, Zhang, Ge, Ku, Max, Soni, Achint, Siu, Sherman, Chen, Haonan, Chandra, Abhranil, Jiang, Ziyan, Arulraj, Aaran, Wang, Kai, Do, Quy Duc, Ni, Yuansheng, Lyu, Bohan, Narsupalli, Yaswanth, Fan, Rongqi, Lyu, Zhiheng, Lin, Yuchen, Chen, Wenhu
The recent years have witnessed great advances in video generation. However, the development of automatic video metrics is lagging significantly behind. None of the existing metric is able to provide reliable scores over generated videos. The main ba
Externí odkaz:
http://arxiv.org/abs/2406.15252
This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. This framework features specialized LLM agents tasked with observing
Externí odkaz:
http://arxiv.org/abs/2405.18092
Autor:
Mao, Kelong, Deng, Chenlong, Chen, Haonan, Mo, Fengran, Liu, Zheng, Sakai, Tetsuya, Dou, Zhicheng
Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization capability of large language models to robustly represent complex conv
Externí odkaz:
http://arxiv.org/abs/2404.13556