Zobrazeno 1 - 10
of 537
pro vyhledávání: '"Guo Jiaming"'
Autor:
Peng, Long, Li, Wenbo, Guo, Jiaming, Di, Xin, Sun, Haoze, Li, Yong, Pei, Renjing, Wang, Yang, Cao, Yang, Zha, Zheng-Jun
Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods primarily focus on generating details from the LR RGB domain, often le
Externí odkaz:
http://arxiv.org/abs/2411.10798
Autor:
Huang, Lei, Guo, Jiaming, He, Guanhua, Zhang, Xishan, Zhang, Rui, Peng, Shaohui, Liu, Shaoli, Chen, Tianshi
Generating long-term texts such as novels using artificial intelligence has always been a challenge. A common approach is to use large language models (LLMs) to construct a hierarchical framework that first plans and then writes. Despite the fact tha
Externí odkaz:
http://arxiv.org/abs/2408.08506
Autor:
Gao, Haihan, Zhang, Rui, Yi, Qi, Yao, Hantao, Li, Haochen, Guo, Jiaming, Peng, Shaohui, Gao, Yunkai, Wang, QiCheng, Hu, Xing, Wen, Yuanbo, Zhang, Zihao, Du, Zidong, Li, Ling, Guo, Qi, Chen, Yunji
Overfitting in RL has become one of the main obstacles to applications in reinforcement learning(RL). Existing methods do not provide explicit semantic constrain for the feature extractor, hindering the agent from learning a unified cross-domain repr
Externí odkaz:
http://arxiv.org/abs/2406.03250
Autor:
Guo, Yuxuan, Peng, Shaohui, Guo, Jiaming, Huang, Di, Zhang, Xishan, Zhang, Rui, Hao, Yifan, Li, Ling, Tian, Zikang, Gao, Mingju, Li, Yutai, Gan, Yiming, Liang, Shuai, Zhang, Zihao, Du, Zidong, Guo, Qi, Hu, Xing, Chen, Yunji
Building open agents has always been the ultimate goal in AI research, and creative agents are the more enticing. Existing LLM agents excel at long-horizon tasks with well-defined goals (e.g., `mine diamonds' in Minecraft). However, they encounter di
Externí odkaz:
http://arxiv.org/abs/2405.15414
Autor:
Peng, Long, Cao, Yang, Pei, Renjing, Li, Wenbo, Guo, Jiaming, Fu, Xueyang, Wang, Yang, Zha, Zheng-Jun
Real-SR endeavors to produce high-resolution images with rich details while mitigating the impact of multiple degradation factors. Although existing methods have achieved impressive achievements in detail recovery, they still fall short when addressi
Externí odkaz:
http://arxiv.org/abs/2405.07023
Autor:
Conde, Marcos V., Lei, Zhijun, Li, Wen, Stejerean, Cosmin, Katsavounidis, Ioannis, Timofte, Radu, Yoon, Kihwan, Gankhuyag, Ganzorig, Lv, Jiangtao, Sun, Long, Pan, Jinshan, Dong, Jiangxin, Tang, Jinhui, Li, Zhiyuan, Wei, Hao, Ge, Chenyang, Zhang, Dongyang, Liu, Tianle, Chen, Huaian, Jin, Yi, Zhou, Menghan, Yan, Yiqiang, Gao, Si, Wu, Biao, Liu, Shaoli, Zheng, Chengjian, Zhang, Diankai, Wang, Ning, Qiu, Xintao, Zhou, Yuanbo, Wu, Kongxian, Dai, Xinwei, Tang, Hui, Deng, Wei, Gao, Qingquan, Tong, Tong, Lee, Jae-Hyeon, Choi, Ui-Jin, Yan, Min, Liu, Xin, Wang, Qian, Ye, Xiaoqian, Du, Zhan, Zhang, Tiansen, Peng, Long, Guo, Jiaming, Di, Xin, Liao, Bohao, Du, Zhibo, Xia, Peize, Pei, Renjing, Wang, Yang, Cao, Yang, Zha, Zhengjun, Han, Bingnan, Yu, Hongyuan, Wu, Zhuoyuan, Wan, Cheng, Liu, Yuqing, Yu, Haodong, Li, Jizhe, Huang, Zhijuan, Huang, Yuan, Zou, Yajun, Guan, Xianyu, Jia, Qi, Zhang, Heng, Yin, Xuanwu, Zuo, Kunlong, Moon, Hyeon-Cheol, Jeong, Tae-hyun, Yang, Yoonmo, Kim, Jae-Gon, Jeong, Jinwoo, Kim, Sunjei
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a dive
Externí odkaz:
http://arxiv.org/abs/2404.16484
Autor:
Ren, Bin, Li, Yawei, Mehta, Nancy, Timofte, Radu, Yu, Hongyuan, Wan, Cheng, Hong, Yuxin, Han, Bingnan, Wu, Zhuoyuan, Zou, Yajun, Liu, Yuqing, Li, Jizhe, He, Keji, Fan, Chao, Zhang, Heng, Zhang, Xiaolin, Yin, Xuanwu, Zuo, Kunlong, Liao, Bohao, Xia, Peizhe, Peng, Long, Du, Zhibo, Di, Xin, Li, Wangkai, Wang, Yang, Zhai, Wei, Pei, Renjing, Guo, Jiaming, Xu, Songcen, Cao, Yang, Zha, Zhengjun, Wang, Yan, Liu, Yi, Wang, Qing, Zhang, Gang, Zhang, Liou, Zhao, Shijie, Sun, Long, Pan, Jinshan, Dong, Jiangxin, Tang, Jinhui, Liu, Xin, Yan, Min, Wang, Qian, Zhou, Menghan, Yan, Yiqiang, Liu, Yixuan, Chan, Wensong, Tang, Dehua, Zhou, Dong, Wang, Li, Tian, Lu, Emad, Barsoum, Jia, Bohan, Qiao, Junbo, Zhou, Yunshuai, Zhang, Yun, Li, Wei, Lin, Shaohui, Zhou, Shenglong, Chen, Binbin, Liao, Jincheng, Zhao, Suiyi, Zhang, Zhao, Wang, Bo, Luo, Yan, Wei, Yanyan, Li, Feng, Wang, Mingshen, Guan, Jinhan, Hu, Dehua, Yu, Jiawei, Xu, Qisheng, Sun, Tao, Lan, Long, Xu, Kele, Lin, Xin, Yue, Jingtong, Yang, Lehan, Du, Shiyi, Qi, Lu, Ren, Chao, Han, Zeyu, Wang, Yuhan, Chen, Chaolin, Li, Haobo, Zheng, Mingjun, Yang, Zhongbao, Song, Lianhong, Yan, Xingzhuo, Fu, Minghan, Zhang, Jingyi, Li, Baiang, Zhu, Qi, Xu, Xiaogang, Guo, Dan, Guo, Chunle, Chen, Jiadi, Long, Huanhuan, Duanmu, Chunjiang, Lei, Xiaoyan, Liu, Jie, Jia, Weilin, Cao, Weifeng, Zhang, Wenlong, Mao, Yanyu, Guo, Ruilong, Zhang, Nihao, Pandey, Manoj, Chernozhukov, Maksym, Le, Giang, Cheng, Shuli, Wang, Hongyuan, Wei, Ziyan, Tang, Qingting, Wang, Liejun, Li, Yongming, Guo, Yanhui, Xu, Hao, Khatami-Rizi, Akram, Mahmoudi-Aznaveh, Ahmad, Hsu, Chih-Chung, Lee, Chia-Ming, Chou, Yi-Shiuan, Joshi, Amogh, Akalwadi, Nikhil, Malagi, Sampada, Yashaswini, Palani, Desai, Chaitra, Tabib, Ramesh Ashok, Patil, Ujwala, Mudenagudi, Uma
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor
Externí odkaz:
http://arxiv.org/abs/2404.10343
Autor:
Zhao, Yunpu, Zhang, Rui, Li, Wenyi, Huang, Di, Guo, Jiaming, Peng, Shaohui, Hao, Yifan, Wen, Yuanbo, Hu, Xing, Du, Zidong, Guo, Qi, Li, Ling, Chen, Yunji
In the field of natural language processing, the rapid development of large language model (LLM) has attracted more and more attention. LLMs have shown a high level of creativity in various tasks, but the methods for assessing such creativity are ina
Externí odkaz:
http://arxiv.org/abs/2401.12491
Autor:
Guo, Yuxuan, Hao, Yifan, Zhang, Rui, Zhou, Enshuai, Du, Zidong, Zhang, Xishan, Song, Xinkai, Wen, Yuanbo, Zhao, Yongwei, Zhou, Xuehai, Guo, Jiaming, Yi, Qi, Peng, Shaohui, Huang, Di, Chen, Ruizhi, Guo, Qi, Chen, Yunji
Research on emergent communication between deep-learning-based agents has received extensive attention due to its inspiration for linguistics and artificial intelligence. However, previous attempts have hovered around emerging communication under per
Externí odkaz:
http://arxiv.org/abs/2311.04474
Autor:
Gao, Yunkai, Zhang, Rui, Guo, Jiaming, Wu, Fan, Yi, Qi, Peng, Shaohui, Lan, Siming, Chen, Ruizhi, Du, Zidong, Hu, Xing, Guo, Qi, Li, Ling, Chen, Yunji
Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks. However, the context shift problem arises due to the distribution discrepancy between the contexts used
Externí odkaz:
http://arxiv.org/abs/2311.03695