Zobrazeno 1 - 10
of 54 712
pro vyhledávání: '"Li, Chen An"'
Autor:
Li, Chen, Rajendran, Bipin.
We present Noise Adaptor, a novel method for constructing competitive low-latency spiking neural networks (SNNs) by converting noise-injected, low-bit artificial neural networks (ANNs). This approach builds on existing ANN-to-SNN conversion technique
Externí odkaz:
http://arxiv.org/abs/2411.17431
Analog in-memory computing (AIMC) has emerged as a promising solution to overcome the von Neumann bottleneck, accelerating neural network computations and improving computational efficiency. While AIMC has demonstrated success with architectures such
Externí odkaz:
http://arxiv.org/abs/2411.17367
Autor:
Wang, Yu-Tong, Ye, Qi-Hang, Yan, Jun-Yong, Qiao, Yufei, Chen, Chen, Cheng, Xiao-Tian, Li, Chen-Hui, Zhang, Zi-Jian, Huang, Cheng-Nian, Meng, Yun, Zou, Kai, Zhan, Wen-Kang, Zhao, Chao, Hu, Xiaolong, Tee, Clarence Augustine T H, Sha, Wei E. I., Huang, Zhixiang, Liu, Huiyun, Jin, Chao-Yuan, Ying, Lei, Liu, Feng
Quantum emitters are a key component in photonic quantum technologies. Enhancing their single-photon emission by engineering the photonic environment using cavities can significantly improve the overall efficiency in quantum information processing. H
Externí odkaz:
http://arxiv.org/abs/2411.16830
Autor:
Li, Zhuo, Luo, Mingshuang, Hou, Ruibing, Zhao, Xin, Liu, Hao, Chang, Hong, Liu, Zimo, Li, Chen
Human motion generation plays a vital role in applications such as digital humans and humanoid robot control. However, most existing approaches disregard physics constraints, leading to the frequent production of physically implausible motions with p
Externí odkaz:
http://arxiv.org/abs/2411.14951
Autor:
Cheng, Jikang, Yan, Zhiyuan, Zhang, Ying, Hao, Li, Ai, Jiaxin, Zou, Qin, Li, Chen, Wang, Zhongyuan
The rapid advancement of face forgery techniques has introduced a growing variety of forgeries. Incremental Face Forgery Detection (IFFD), involving gradually adding new forgery data to fine-tune the previously trained model, has been introduced as a
Externí odkaz:
http://arxiv.org/abs/2411.11396
Autor:
Lien, Wei-Hsiang, Chandra, Benedictus Kent, Fischer, Robin, Tang, Ya-Hui, Wang, Shiann-Jang, Hsu, Wei-En, Fu, Li-Chen
In recent years, with the rapid development of augmented reality (AR) technology, there is an increasing demand for multi-user collaborative experiences. Unlike for single-user experiences, ensuring the spatial localization of every user and maintain
Externí odkaz:
http://arxiv.org/abs/2411.10940
We introduce DiHuR, a novel Diffusion-guided model for generalizable Human 3D Reconstruction and view synthesis from sparse, minimally overlapping images. While existing generalizable human radiance fields excel at novel view synthesis, they often st
Externí odkaz:
http://arxiv.org/abs/2411.11903
Autor:
Yang, Chih-Kai, Fu, Yu-Kuan, Li, Chen-An, Lin, Yi-Cheng, Lin, Yu-Xiang, Chen, Wei-Chih, Chung, Ho Lam, Kuan, Chun-Yi, Huang, Wei-Ping, Lu, Ke-Han, Lin, Tzu-Quan, Wang, Hsiu-Hsuan, Hu, En-Pei, Hsu, Chan-Jan, Tseng, Liang-Hsuan, Chiu, I-Hsiang, Sanga, Ulin, Chen, Xuanjun, Hsu, Po-chun, Yang, Shu-wen, Lee, Hung-yi
This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech interaction in multi-turn conversations. Our end-to-end model incorpor
Externí odkaz:
http://arxiv.org/abs/2411.07111
Autor:
Huang, Chien-yu, Chen, Wei-Chih, Yang, Shu-wen, Liu, Andy T., Li, Chen-An, Lin, Yu-Xiang, Tseng, Wei-Cheng, Diwan, Anuj, Shih, Yi-Jen, Shi, Jiatong, Chen, William, Chen, Xuanjun, Hsiao, Chi-Yuan, Peng, Puyuan, Wang, Shih-Heng, Kuan, Chun-Yi, Lu, Ke-Han, Chang, Kai-Wei, Yang, Chih-Kai, Ritter-Gutierrez, Fabian, Chuang, Ming To, Huang, Kuan-Po, Arora, Siddhant, Lin, You-Kuan, Yeo, Eunjung, Chang, Kalvin, Chien, Chung-Ming, Choi, Kwanghee, Hsieh, Cheng-Hsiu, Lin, Yi-Cheng, Yu, Chee-En, Chiu, I-Hsiang, Guimarães, Heitor R., Han, Jionghao, Lin, Tzu-Quan, Lin, Tzu-Yuan, Chang, Homu, Chang, Ting-Wu, Chen, Chun Wei, Chen, Shou-Jen, Chen, Yu-Hua, Cheng, Hsi-Chun, Dhawan, Kunal, Fang, Jia-Lin, Fang, Shi-Xin, Chiang, Kuan-Yu Fang, Fu, Chi An, Hsiao, Hsien-Fu, Hsu, Ching Yu, Huang, Shao-Syuan, Wei, Lee Chen, Lin, Hsi-Che, Lin, Hsuan-Hao, Lin, Hsuan-Ting, Lin, Jian-Ren, Liu, Ting-Chun, Lu, Li-Chun, Pai, Tsung-Min, Pasad, Ankita, Kuan, Shih-Yun Shan, Shon, Suwon, Tang, Yuxun, Tsai, Yun-Shao, Wei, Jui-Chiang, Wei, Tzu-Chieh, Wu, Chengxi, Wu, Dien-Ruei, Yang, Chao-Han Huck, Yang, Chieh-Chi, Yip, Jia Qi, Yuan, Shao-Xiang, Noroozi, Vahid, Chen, Zhehuai, Wu, Haibin, Livescu, Karen, Harwath, David, Watanabe, Shinji, Lee, Hung-yi
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spoken language model that comprehends a wide range of natural language i
Externí odkaz:
http://arxiv.org/abs/2411.05361
The past year has witnessed the significant advancement of video-based large language models. However, the challenge of developing a unified model for both short and long video understanding remains unresolved. Most existing video LLMs cannot handle
Externí odkaz:
http://arxiv.org/abs/2411.02327