Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Wu, Zongwei"'
Autor:
Tan, Yuedong, Wu, Zongwei, Fu, Yuqian, Zhou, Zhuyun, Sun, Guolei, Ma, Chao, Paudel, Danda Pani, Van Gool, Luc, Timofte, Radu
With the emergence of a single large model capable of successfully solving a multitude of tasks in NLP, there has been growing research interest in achieving similar goals in computer vision. On the one hand, most of these generic models, referred to
Externí odkaz:
http://arxiv.org/abs/2405.17773
Reconstructing missing details from degraded low-quality inputs poses a significant challenge. Recent progress in image restoration has demonstrated the efficacy of learning large models capable of addressing various degradations simultaneously. None
Externí odkaz:
http://arxiv.org/abs/2405.15475
Autor:
Dai, Yuekun, Zhang, Dafeng, Li, Xiaoming, Yue, Zongsheng, Li, Chongyi, Zhou, Shangchen, Feng, Ruicheng, Yang, Peiqing, Jin, Zhezhu, Liu, Guanqun, Loy, Chen Change, Zhang, Lize, Liu, Shuai, Feng, Chaoyu, Wang, Luyang, Chen, Shuan, Shao, Guangqi, Wang, Xiaotao, Lei, Lei, Yang, Qirui, Cheng, Qihua, Xu, Zhiqiang, Liu, Yihao, Yue, Huanjing, Yang, Jingyu, Vasluianu, Florin-Alexandru, Wu, Zongwei, Ciubotariu, George, Timofte, Radu, Zhang, Zhao, Zhao, Suiyi, Wang, Bo, Zuo, Zhichao, Wei, Yanyan, Teja, Kuppa Sai Sri, A, Jayakar Reddy, Rongali, Girish, Mitra, Kaushik, Ma, Zhihao, Liu, Yongxu, Zhang, Wanying, Shang, Wei, He, Yuhong, Peng, Long, Yu, Zhongxin, Luo, Shaofei, Wang, Jian, Miao, Yuqi, Li, Baiang, Wei, Gang, Verma, Rakshank, Maheshwari, Ritik, Tekchandani, Rahul, Hambarde, Praful, Tazi, Satya Narayan, Vipparthi, Santosh Kumar, Murala, Subrahmanyam, Zhang, Haopeng, Hou, Yingli, Yao, Mingde, S, Levin M, Sundararajan, Aniruth, A, Hari Kumar
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data fo
Externí odkaz:
http://arxiv.org/abs/2404.19534
Autor:
Liu, Xiaoning, Wu, Zongwei, Li, Ao, Vasluianu, Florin-Alexandru, Zhang, Yulun, Gu, Shuhang, Zhang, Le, Zhu, Ce, Timofte, Radu, Jin, Zhi, Wu, Hongjun, Wang, Chenxi, Ling, Haitao, Cai, Yuanhao, Bian, Hao, Zheng, Yuxin, Lin, Jing, Yuille, Alan, Shao, Ben, Guo, Jin, Liu, Tianli, Wu, Mohao, Feng, Yixu, Hou, Shuo, Lin, Haotian, Zhu, Yu, Wu, Peng, Dong, Wei, Sun, Jinqiu, Zhang, Yanning, Yan, Qingsen, Zou, Wenbin, Yang, Weipeng, Li, Yunxiang, Wei, Qiaomu, Ye, Tian, Chen, Sixiang, Zhang, Zhao, Zhao, Suiyi, Wang, Bo, Luo, Yan, Zuo, Zhichao, Wang, Mingshen, Wang, Junhu, Wei, Yanyan, Sun, Xiaopeng, Gao, Yu, Huang, Jiancheng, Chen, Hongming, Chen, Xiang, Tang, Hui, Chen, Yuanbin, Zhou, Yuanbo, Dai, Xinwei, Qiu, Xintao, Deng, Wei, Gao, Qinquan, Tong, Tong, Li, Mingjia, Hu, Jin, He, Xinyu, Guo, Xiaojie, Sabarinathan, Uma, K, Sasithradevi, A, Bama, B Sathya, Roomi, S. Mohamed Mansoor, Srivatsav, V., Wang, Jinjuan, Sun, Long, Chen, Qiuying, Shao, Jiahong, Zhang, Yizhi, Conde, Marcos V., Feijoo, Daniel, Benito, Juan C., García, Alvaro, Lee, Jaeho, Kim, Seongwan, A, Sharif S M, Khujaev, Nodirkhuja, Tsoy, Roman, Murtaza, Ali, Khairuddin, Uswah, Faudzi, Ahmad 'Athif Mohd, Malagi, Sampada, Joshi, Amogh, Akalwadi, Nikhil, Desai, Chaitra, Tabib, Ramesh Ashok, Mudenagudi, Uma, Lian, Wenyi, Lian, Wenjing, Kalyanshetti, Jagadeesh, Aralikatti, Vijayalaxmi Ashok, Yashaswini, Palani, Upasi, Nitish, Hegde, Dikshit, Patil, Ujwala, C, Sujata, Yan, Xingzhuo, Hao, Wei, Fu, Minghan, choksy, Pooja, Sarvaiya, Anjali, Upla, Kishor, Raja, Kiran, Yan, Hailong, Zhang, Yunkai, Li, Baiang, Zhang, Jingyi, Zheng, Huan
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and vi
Externí odkaz:
http://arxiv.org/abs/2404.14248
Autor:
Wang, Zuowen, Gao, Chang, Wu, Zongwei, Conde, Marcos V., Timofte, Radu, Liu, Shih-Chii, Chen, Qinyu, Zha, Zheng-jun, Zhai, Wei, Han, Han, Liao, Bohao, Wu, Yuliang, Wan, Zengyu, Wang, Zhong, Cao, Yang, Tan, Ganchao, Chen, Jinze, Pei, Yan Ru, Brüers, Sasskia, Crouzet, Sébastien, McLelland, Douglas, Coenen, Oliver, Zhang, Baoheng, Gao, Yizhao, Li, Jingyuan, So, Hayden Kwok-Hay, Bich, Philippe, Boretti, Chiara, Prono, Luciano, Lică, Mircea, Dinucu-Jianu, David, Grîu, Cătălin, Lin, Xiaopeng, Ren, Hongwei, Cheng, Bojun, Zhang, Xinan, Vial, Valentin, Yezzi, Anthony, Tsai, James
This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tra
Externí odkaz:
http://arxiv.org/abs/2404.11770
Autor:
Chen, Zheng, Wu, Zongwei, Zamfir, Eduard, Zhang, Kai, Zhang, Yulun, Timofte, Radu, Yang, Xiaokang, Yu, Hongyuan, Wan, Cheng, Hong, Yuxin, Huang, Zhijuan, Zou, Yajun, Huang, Yuan, Lin, Jiamin, Han, Bingnan, Guan, Xianyu, Yu, Yongsheng, Zhang, Daoan, Yin, Xuanwu, Zuo, Kunlong, Hao, Jinhua, Zhao, Kai, Yuan, Kun, Sun, Ming, Zhou, Chao, An, Hongyu, Zhang, Xinfeng, Song, Zhiyuan, Dong, Ziyue, Zhao, Qing, Xu, Xiaogang, Wei, Pengxu, Dou, Zhi-chao, Wang, Gui-ling, Hsu, Chih-Chung, Lee, Chia-Ming, Chou, Yi-Shiuan, Korkmaz, Cansu, Tekalp, A. Murat, Wei, Yubin, Yan, Xiaole, Li, Binren, Chen, Haonan, Zhang, Siqi, Chen, Sihan, Joshi, Amogh, Akalwadi, Nikhil, Malagi, Sampada, Yashaswini, Palani, Desai, Chaitra, Tabib, Ramesh Ashok, Patil, Ujwala, Mudenagudi, Uma, Sarvaiya, Anjali, Choksy, Pooja, Joshi, Jagrit, Kawa, Shubh, Upla, Kishor, Patwardhan, Sushrut, Ramachandra, Raghavendra, Hossain, Sadat, Park, Geongi, Uddin, S. M. Nadim, Xu, Hao, Guo, Yanhui, Urumbekov, Aman, Yan, Xingzhuo, Hao, Wei, Fu, Minghan, Orais, Isaac, Smith, Samuel, Liu, Ying, Jia, Wangwang, Xu, Qisheng, Xu, Kele, Yuan, Weijun, Li, Zhan, Kuang, Wenqin, Guan, Ruijin, Deng, Ruting, Zhang, Zhao, Wang, Bo, Zhao, Suiyi, Luo, Yan, Wei, Yanyan, Khan, Asif Hussain, Micheloni, Christian, Martinel, Niki
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of fo
Externí odkaz:
http://arxiv.org/abs/2404.09790
Lighting normalization is a crucial but underexplored restoration task with broad applications. However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, th
Externí odkaz:
http://arxiv.org/abs/2403.18730
Autor:
Liu, Xiaoning, Li, Ao, Wu, Zongwei, Du, Yapeng, Zhang, Le, Zhang, Yulun, Timofte, Radu, Zhu, Ce
Leveraging Transformer attention has led to great advancements in HDR deghosting. However, the intricate nature of self-attention introduces practical challenges, as existing state-of-the-art methods often demand high-end GPUs or exhibit slow inferen
Externí odkaz:
http://arxiv.org/abs/2403.10376
Autor:
An, Zhaochong, Sun, Guolei, Liu, Yun, Liu, Fayao, Wu, Zongwei, Wang, Dan, Van Gool, Luc, Belongie, Serge
This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. The former arises from non-uniform point sampling, allowing
Externí odkaz:
http://arxiv.org/abs/2403.00592
Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses a significant challenge in image super-resolution (SR). While recent approaches have demonstrated the efficacy of intricate operations customized for various objectives,
Externí odkaz:
http://arxiv.org/abs/2402.03412