no code implementations • 23 Mar 2024 • Hao Wang, Tang Li, Chenhui Chu, Nengjun Zhu, Rui Wang, Pinpin Zhu
This approach aims to generate relation representations that are more aware of the spatial context and unseen relation in a manner similar to human perception.
no code implementations • 5 Jun 2023 • Qianqian Dong, Zhiying Huang, Qiao Tian, Chen Xu, Tom Ko, Yunlong Zhao, Siyuan Feng, Tang Li, Kexin Wang, Xuxin Cheng, Fengpeng Yue, Ye Bai, Xi Chen, Lu Lu, Zejun Ma, Yuping Wang, Mingxuan Wang, Yuxuan Wang
For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model.
1 code implementation • CVPR 2023 • Tang Li, Fengchun Qiao, Mengmeng Ma, Xi Peng
How to develop robust explanations against out-of-distribution data?
no code implementations • 27 Oct 2022 • Yuanzhe Chen, Ming Tu, Tang Li, Xin Li, Qiuqiang Kong, Jiaxin Li, Zhichao Wang, Qiao Tian, Yuping Wang, Yuxuan Wang
In this paper, we propose to use intermediate bottleneck features (IBFs) to replace PPGs.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 17 Dec 2021 • Tang Li, Jing Gao, Xi Peng
Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization.
no code implementations • 28 Feb 2019 • Qiuwen Lou, Indranil Palit, Tang Li, Andras Horvath, Michael Niemier, X. Sharon Hu
While it is well-known that CeNNs can be well-suited for spatio-temporal information processing, few (if any) studies have quantified the energy/delay/accuracy of a CeNN-friendly algorithm and compared the CeNN-based approach to the best von Neumann algorithm at the application level.