Zobrazeno 1 - 10
of 3 596
pro vyhledávání: '"Wang, Xiaogang"'
Autor:
Zeng, Wang, Jin, Sheng, Xu, Lumin, Liu, Wentao, Qian, Chen, Ouyang, Wanli, Luo, Ping, Wang, Xiaogang
Transformers are widely used in computer vision areas and have achieved remarkable success. Most state-of-the-art approaches split images into regular grids and represent each grid region with a vision token. However, fixed token distribution disrega
Externí odkaz:
http://arxiv.org/abs/2407.11321
The design and analysis of Computer-Aided Design (CAD) sketches play a crucial role in industrial product design, primarily involving CAD primitives and their inter-primitive constraints. To address challenges related to error accumulation in autoreg
Externí odkaz:
http://arxiv.org/abs/2407.00410
Autor:
Li, Yinglong, Wu, Hongyu, Wang, Xiaogang, Qin, Qingzhao, Zhao, Yijiao, wang, Yong, Hao, Aimin
We propose FaceCom, a method for 3D facial shape completion, which delivers high-fidelity results for incomplete facial inputs of arbitrary forms. Unlike end-to-end shape completion methods based on point clouds or voxels, our approach relies on a me
Externí odkaz:
http://arxiv.org/abs/2406.02074
Autor:
Wang, Fu-Yun, Huang, Zhaoyang, Bergman, Alexander William, Shen, Dazhong, Gao, Peng, Lingelbach, Michael, Sun, Keqiang, Bian, Weikang, Song, Guanglu, Liu, Yu, Li, Hongsheng, Wang, Xiaogang
The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfact
Externí odkaz:
http://arxiv.org/abs/2405.18407
The concept of guiding center is potent in astrophysics, space plasmas, fusion researches, and arc plasmas to solve the multi-scale dynamics of magnetized plasmas. In this letter, we rigorously prove that the guiding center dynamics can generally be
Externí odkaz:
http://arxiv.org/abs/2403.02883
This work introduces a new Transformer model called Cached Transformer, which uses Gated Recurrent Cached (GRC) attention to extend the self-attention mechanism with a differentiable memory cache of tokens. GRC attention enables attending to both pas
Externí odkaz:
http://arxiv.org/abs/2312.12742
Autor:
Wang, Wenhai, Xie, Jiangwei, Hu, ChuanYang, Zou, Haoming, Fan, Jianan, Tong, Wenwen, Wen, Yang, Wu, Silei, Deng, Hanming, Li, Zhiqi, Tian, Hao, Lu, Lewei, Zhu, Xizhou, Wang, Xiaogang, Qiao, Yu, Dai, Jifeng
Large language models (LLMs) have opened up new possibilities for intelligent agents, endowing them with human-like thinking and cognitive abilities. In this work, we delve into the potential of large language models (LLMs) in autonomous driving (AD)
Externí odkaz:
http://arxiv.org/abs/2312.09245
Autor:
Li, Hao, Yang, Xue, Wang, Zhaokai, Zhu, Xizhou, Zhou, Jie, Qiao, Yu, Wang, Xiaogang, Li, Hongsheng, Lu, Lewei, Dai, Jifeng
Many reinforcement learning environments (e.g., Minecraft) provide only sparse rewards that indicate task completion or failure with binary values. The challenge in exploration efficiency in such environments makes it difficult for reinforcement-lear
Externí odkaz:
http://arxiv.org/abs/2312.09238
Autor:
Cai, Zhongang, Jiang, Jianping, Qing, Zhongfei, Guo, Xinying, Zhang, Mingyuan, Lin, Zhengyu, Mei, Haiyi, Wei, Chen, Wang, Ruisi, Yin, Wanqi, Fan, Xiangyu, Du, Han, Pan, Liang, Gao, Peng, Yang, Zhitao, Gao, Yang, Li, Jiaqi, Ren, Tianxiang, Wei, Yukun, Wang, Xiaogang, Loy, Chen Change, Yang, Lei, Liu, Ziwei
In this work, we present Digital Life Project, a framework utilizing language as the universal medium to build autonomous 3D characters, who are capable of engaging in social interactions and expressing with articulated body motions, thereby simulati
Externí odkaz:
http://arxiv.org/abs/2312.04547
Autor:
Zheng, Mingkai, You, Shan, Huang, Lang, Su, Xiu, Wang, Fei, Qian, Chen, Wang, Xiaogang, Xu, Chang
Image classification is a longstanding problem in computer vision and machine learning research. Most recent works (e.g. SupCon , Triplet, and max-margin) mainly focus on grouping the intra-class samples aggressively and compactly, with the assumptio
Externí odkaz:
http://arxiv.org/abs/2308.10761