Zobrazeno 1 - 10
of 5 539
pro vyhledávání: '"ZHANG, Jianwei"'
Autor:
Qwen, Yang, An, Yang, Baosong, Zhang, Beichen, Hui, Binyuan, Zheng, Bo, Yu, Bowen, Li, Chengyuan, Liu, Dayiheng, Huang, Fei, Wei, Haoran, Lin, Huan, Yang, Jian, Tu, Jianhong, Zhang, Jianwei, Yang, Jianxin, Yang, Jiaxi, Zhou, Jingren, Lin, Junyang, Dang, Kai, Lu, Keming, Bao, Keqin, Yang, Kexin, Yu, Le, Li, Mei, Xue, Mingfeng, Zhang, Pei, Zhu, Qin, Men, Rui, Lin, Runji, Li, Tianhao, Xia, Tingyu, Ren, Xingzhang, Ren, Xuancheng, Fan, Yang, Su, Yang, Zhang, Yichang, Wan, Yu, Liu, Yuqiong, Cui, Zeyu, Zhang, Zhenru, Qiu, Zihan
In this report, we introduce Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has been significantly improved during both the pre-training and post-training stag
Externí odkaz:
http://arxiv.org/abs/2412.15115
Autor:
Kong, Weijie, Tian, Qi, Zhang, Zijian, Min, Rox, Dai, Zuozhuo, Zhou, Jin, Xiong, Jiangfeng, Li, Xin, Wu, Bo, Zhang, Jianwei, Wu, Kathrina, Lin, Qin, Yuan, Junkun, Long, Yanxin, Wang, Aladdin, Wang, Andong, Li, Changlin, Huang, Duojun, Yang, Fang, Tan, Hao, Wang, Hongmei, Song, Jacob, Bai, Jiawang, Wu, Jianbing, Xue, Jinbao, Wang, Joey, Wang, Kai, Liu, Mengyang, Li, Pengyu, Li, Shuai, Wang, Weiyan, Yu, Wenqing, Deng, Xinchi, Li, Yang, Chen, Yi, Cui, Yutao, Peng, Yuanbo, Yu, Zhentao, He, Zhiyu, Xu, Zhiyong, Zhou, Zixiang, Xu, Zunnan, Tao, Yangyu, Lu, Qinglin, Liu, Songtao, Zhou, Daquan, Wang, Hongfa, Yang, Yong, Wang, Di, Liu, Yuhong, Jiang, Jie, Zhong, Caesar
Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilitie
Externí odkaz:
http://arxiv.org/abs/2412.03603
Generating high-quality meshes with complex structures and realistic surfaces is the primary goal of 3D generative models. Existing methods typically employ sequence data or deformable tetrahedral grids for mesh generation. However, sequence-based me
Externí odkaz:
http://arxiv.org/abs/2410.17802
Autor:
Liu, Shang-Ching, Tran, Van Nhiem, Chen, Wenkai, Cheng, Wei-Lun, Huang, Yen-Lin, Liao, I-Bin, Li, Yung-Hui, Zhang, Jianwei
Affordance understanding, the task of identifying actionable regions on 3D objects, plays a vital role in allowing robotic systems to engage with and operate within the physical world. Although Visual Language Models (VLMs) have excelled in high-leve
Externí odkaz:
http://arxiv.org/abs/2410.11564
Autor:
Yang, Jiuzheng, Tang, Song, Zhang, Yangkuiyi, Li, Shuaifeng, Ye, Mao, Zhang, Jianwei, Zhu, Xiatian
Source-Free domain adaptive Object Detection (SFOD) aims to transfer a detector (pre-trained on source domain) to new unlabelled target domains. Current SFOD methods typically follow the Mean Teacher framework, where weak-to-strong augmentation provi
Externí odkaz:
http://arxiv.org/abs/2410.05557
Large language models have demonstrated promising capabilities upon scaling up parameters. However, serving large language models incurs substantial computation and memory movement costs due to their large scale. Quantization methods have been employ
Externí odkaz:
http://arxiv.org/abs/2409.20361
Autor:
Bai, Kaixin, Zeng, Huajian, Zhang, Lei, Liu, Yiwen, Xu, Hongli, Chen, Zhaopeng, Zhang, Jianwei
Transparent object depth perception poses a challenge in everyday life and logistics, primarily due to the inability of standard 3D sensors to accurately capture depth on transparent or reflective surfaces. This limitation significantly affects depth
Externí odkaz:
http://arxiv.org/abs/2409.08926
Learning policies for multi-entity systems in 3D environments is far more complicated against single-entity scenarios, due to the exponential expansion of the global state space as the number of entities increases. One potential solution of alleviati
Externí odkaz:
http://arxiv.org/abs/2407.12505
Despite the substantial progress in deep learning, its adoption in industrial robotics projects remains limited, primarily due to challenges in data acquisition and labeling. Previous sim2real approaches using domain randomization require extensive s
Externí odkaz:
http://arxiv.org/abs/2407.12449
Autor:
Yang, An, Yang, Baosong, Hui, Binyuan, Zheng, Bo, Yu, Bowen, Zhou, Chang, Li, Chengpeng, Li, Chengyuan, Liu, Dayiheng, Huang, Fei, Dong, Guanting, Wei, Haoran, Lin, Huan, Tang, Jialong, Wang, Jialin, Yang, Jian, Tu, Jianhong, Zhang, Jianwei, Ma, Jianxin, Yang, Jianxin, Xu, Jin, Zhou, Jingren, Bai, Jinze, He, Jinzheng, Lin, Junyang, Dang, Kai, Lu, Keming, Chen, Keqin, Yang, Kexin, Li, Mei, Xue, Mingfeng, Ni, Na, Zhang, Pei, Wang, Peng, Peng, Ru, Men, Rui, Gao, Ruize, Lin, Runji, Wang, Shijie, Bai, Shuai, Tan, Sinan, Zhu, Tianhang, Li, Tianhao, Liu, Tianyu, Ge, Wenbin, Deng, Xiaodong, Zhou, Xiaohuan, Ren, Xingzhang, Zhang, Xinyu, Wei, Xipin, Ren, Xuancheng, Liu, Xuejing, Fan, Yang, Yao, Yang, Zhang, Yichang, Wan, Yu, Chu, Yunfei, Liu, Yuqiong, Cui, Zeyu, Zhang, Zhenru, Guo, Zhifang, Fan, Zhihao
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models. We release a comprehensive suite of foundational and instruction-tuned language models, encompassing a parameter range from 0.5 to
Externí odkaz:
http://arxiv.org/abs/2407.10671