Zobrazeno 1 - 10
of 1 394
pro vyhledávání: '"LIU Zhijian"'
Publikováno v:
电力工程技术, Vol 41, Iss 5, Pp 12-20 (2022)
Aiming at the ineffective suppression results of sub-synchronous oscillation under grid connection of doubly-fed wind turbines process with traditional method,a virtual resistance-based fractional-order proportional integral (FOPI) control strategy i
Externí odkaz:
https://doaj.org/article/687bff2b554742fc845d6f088aac9d0d
Autor:
Liu, Zhijian, Zhu, Ligeng, Shi, Baifeng, Zhang, Zhuoyang, Lou, Yuming, Yang, Shang, Xi, Haocheng, Cao, Shiyi, Gu, Yuxian, Li, Dacheng, Li, Xiuyu, Fang, Yunhao, Chen, Yukang, Hsieh, Cheng-Yu, Huang, De-An, Cheng, An-Chieh, Nath, Vishwesh, Hu, Jinyi, Liu, Sifei, Krishna, Ranjay, Xu, Daguang, Wang, Xiaolong, Molchanov, Pavlo, Kautz, Jan, Yin, Hongxu, Han, Song, Lu, Yao
Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a family of open VLMs designed to optimize both efficiency and accuracy
Externí odkaz:
http://arxiv.org/abs/2412.04468
Autor:
Nath, Vishwesh, Li, Wenqi, Yang, Dong, Myronenko, Andriy, Zheng, Mingxin, Lu, Yao, Liu, Zhijian, Yin, Hongxu, Law, Yee Man, Tang, Yucheng, Guo, Pengfei, Zhao, Can, Xu, Ziyue, He, Yufan, Heinrich, Greg, Aylward, Stephen, Edgar, Marc, Zephyr, Michael, Molchanov, Pavlo, Turkbey, Baris, Roth, Holger, Xu, Daguang
Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is essential. In traditional computer vision tasks, creative or approximate a
Externí odkaz:
http://arxiv.org/abs/2411.12915
Autor:
Chen, Yukang, Xue, Fuzhao, Li, Dacheng, Hu, Qinghao, Zhu, Ligeng, Li, Xiuyu, Fang, Yunhao, Tang, Haotian, Yang, Shang, Liu, Zhijian, He, Ethan, Yin, Hongxu, Molchanov, Pavlo, Kautz, Jan, Fan, Linxi, Zhu, Yuke, Lu, Yao, Han, Song
Long-context capability is critical for multi-modal foundation models, especially for long video understanding. We introduce LongVILA, a full-stack solution for long-context visual-language models by co-designing the algorithm and system. For model t
Externí odkaz:
http://arxiv.org/abs/2408.10188
Autor:
Liu, Zhijian, Zhang, Zhuoyang, Khaki, Samir, Yang, Shang, Tang, Haotian, Xu, Chenfeng, Keutzer, Kurt, Han, Song
Semantic segmentation empowers numerous real-world applications, such as autonomous driving and augmented/mixed reality. These applications often operate on high-resolution images (e.g., 8 megapixels) to capture the fine details. However, this comes
Externí odkaz:
http://arxiv.org/abs/2407.19014
We present LidarDM, a novel LiDAR generative model capable of producing realistic, layout-aware, physically plausible, and temporally coherent LiDAR videos. LidarDM stands out with two unprecedented capabilities in LiDAR generative modeling: (i) LiDA
Externí odkaz:
http://arxiv.org/abs/2404.02903
Autor:
Kodaira, Akio, Xu, Chenfeng, Hazama, Toshiki, Yoshimoto, Takanori, Ohno, Kohei, Mitsuhori, Shogo, Sugano, Soichi, Cho, Hanying, Liu, Zhijian, Keutzer, Kurt
We introduce StreamDiffusion, a real-time diffusion pipeline designed for interactive image generation. Existing diffusion models are adept at creating images from text or image prompts, yet they often fall short in real-time interaction. This limita
Externí odkaz:
http://arxiv.org/abs/2312.12491
Autor:
Wu, Xiaoyang, Jiang, Li, Wang, Peng-Shuai, Liu, Zhijian, Liu, Xihui, Qiao, Yu, Ouyang, Wanli, He, Tong, Zhao, Hengshuang
This paper is not motivated to seek innovation within the attention mechanism. Instead, it focuses on overcoming the existing trade-offs between accuracy and efficiency within the context of point cloud processing, leveraging the power of scale. Draw
Externí odkaz:
http://arxiv.org/abs/2312.10035
Autor:
Tang, Haotian, Yang, Shang, Liu, Zhijian, Hong, Ke, Yu, Zhongming, Li, Xiuyu, Dai, Guohao, Wang, Yu, Han, Song
Sparse convolution plays a pivotal role in emerging workloads, including point cloud processing in AR/VR, autonomous driving, and graph understanding in recommendation systems. Since the computation pattern is sparse and irregular, specialized high-p
Externí odkaz:
http://arxiv.org/abs/2311.12862
We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. Typically, training LLMs with long context sizes is computationally expensive, requiring
Externí odkaz:
http://arxiv.org/abs/2309.12307