Zobrazeno 1 - 10
of 1 759
pro vyhledávání: '"Wang, HaoTian"'
Autor:
Wang, Haotian, Weng, Yuzhe, Li, Yueyan, Guo, Zilu, Du, Jun, Niu, Shutong, Ma, Jiefeng, He, Shan, Wu, Xiaoyan, Hu, Qiming, Yin, Bing, Liu, Cong, Liu, Qingfeng
Diffusion models have revolutionized the field of talking head generation, yet still face challenges in expressiveness, controllability, and stability in long-time generation. In this research, we propose an EmotiveTalk framework to address these iss
Externí odkaz:
http://arxiv.org/abs/2411.16726
As mixed with real pulsations, the reflection of super-Nyquist frequencies (SNFs) pose a threat to asteroseismic properties. Although SNFs have been studied in several pulsating stars, a systematic survey remains scarcely explored. Here we propose a
Externí odkaz:
http://arxiv.org/abs/2411.12481
Autor:
Yang, Qinchen, Hong, Zhiqing, Cao, Dongjiang, Wang, Haotian, Xie, Zejun, He, Tian, Liu, Yunhuai, Yang, Yu, Zhang, Desheng
Textual description of a physical location, commonly known as an address, plays an important role in location-based services(LBS) such as on-demand delivery and navigation. However, the prevalence of abnormal addresses, those containing inaccuracies
Externí odkaz:
http://arxiv.org/abs/2411.13584
Heterogeneous graph is an important structure for modeling complex relational data in real-world scenarios and usually involves various node prediction tasks within a single graph. Training these tasks separately may neglect beneficial information sh
Externí odkaz:
http://arxiv.org/abs/2410.22089
Depth completion, inferring dense depth maps from sparse measurements, is crucial for robust 3D perception. Although deep learning based methods have made tremendous progress in this problem, these models cannot generalize well across different scene
Externí odkaz:
http://arxiv.org/abs/2410.18408
In multimodal sentiment analysis, collecting text data is often more challenging than video or audio due to higher annotation costs and inconsistent automatic speech recognition (ASR) quality. To address this challenge, our study has developed a robu
Externí odkaz:
http://arxiv.org/abs/2410.15029
Autor:
Xu, Yingjing, Cai, Xueyan, Zhou, Zihong, Xue, Mengru, Wang, Bo, Wang, Haotian, Li, Zhengke, Weng, Chentian, Luo, Wei, Yao, Cheng, Lin, Bo, Yin, Jianwei
Hypomimia is a non-motor symptom of Parkinson's disease that manifests as delayed facial movements and expressions, along with challenges in articulation and emotion. Currently, subjective evaluation by neurologists is the primary method for hypomimi
Externí odkaz:
http://arxiv.org/abs/2410.09772
Autor:
Chu, Zheng, Chen, Jingchang, Chen, Qianglong, Wang, Haotian, Zhu, Kun, Du, Xiyuan, Yu, Weijiang, Liu, Ming, Qin, Bing
Large language models (LLMs) have demonstrated strong reasoning capabilities. Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks. Retrieval-augmented reasoning represents a promising approach. However, signifi
Externí odkaz:
http://arxiv.org/abs/2406.19820
Autor:
Wang, Xunzhi, Zhang, Zhuowei, Li, Qiongyu, Chen, Gaonan, Hu, Mengting, li, Zhiyu, Luo, Bitong, Gao, Hang, Han, Zhixin, Wang, Haotian
The rapid development of large language models (LLMs) has shown promising practical results. However, their low interpretability often leads to errors in unforeseen circumstances, limiting their utility. Many works have focused on creating comprehens
Externí odkaz:
http://arxiv.org/abs/2406.12784
Autor:
Zhu, Kun, Feng, Xiaocheng, Du, Xiyuan, Gu, Yuxuan, Yu, Weijiang, Wang, Haotian, Chen, Qianglong, Chu, Zheng, Chen, Jingchang, Qin, Bing
Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data. One recent solution is to train a
Externí odkaz:
http://arxiv.org/abs/2406.01549